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13 Problem-Solving Activities & Exercises for Your Team

• May 22, 2024
• Project Management
• 21 min read

Are you looking to enhance your or your team’s problem-solving abilities? Engaging in activities specifically designed to stimulate your and your team’s critical thinking skills can be an excellent way to sharpen your problem-solving prowess. Whether you enjoy puzzles, brain teasers, or interactive challenges, these activities provide an opportunity to overcome obstacles and think creatively.

By immersing yourself in problem-solving activities, you can develop valuable strategies, improve your decision-making abilities, and boost your overall problem-solving IQ. Get ready to unlock your full potential and tackle any challenge that comes your way with these exciting activities for problem-solving.

In this article, we will explore activities for problem-solving that can help enhance your team’s problem-solving skills, allowing you to approach challenges with confidence and creativity.

What Are Problem Solving Activities?

Problem-solving activities or problem-solving exercises are interactive games requiring critical thinking to solve puzzles. They enhance teamwork & critical thinking. Examples include building towers, navigating simulated challenges, and fostering creativity and communication.

For instance, imagine a team working together to construct the tallest tower using limited materials. They strategize, communicate ideas, and problem-solve to create the best structure, promoting collaboration and inventive thinking among team members.

Some widely practiced problem-solving activities include:

• A Shrinking Vessel: Teams must fit into a shrinking space, testing their cooperation and adaptability.
• Marshmallow Spaghetti Tower: Participants build a tower using marshmallows and spaghetti, promoting creative engineering.
• Egg Drop: Protecting an egg from a fall challenges problem-solving skills.
• Desert Island Survival: Teams simulate survival scenarios, encouraging creative solutions.
• Rolling Dice: A simple yet effective game involving chance and decision-making.
• Build a Tower: Constructing a stable tower with limited resources fosters teamwork and innovation, etc.

13 Easy Activities For Problem-Solving Ideas to Enhance Team Collaboration

Team building activities offer a great opportunity to test problem-solving abilities and promote effective collaboration within a group to problem solving group activities. By engaging in these activities, teams can break the monotony of the workplace and create a more inclusive and welcoming environment.

Here are nine easy-to-implement activities that can bring substantial change to your team culture and overall workplace dynamics.

#1. Crossword Puzzles

Objective: To enhance problem-solving skills, vocabulary, and cognitive abilities through engaging crossword puzzles. 

Estimated Time: 15-20 Minutes 

Materials Needed:

• Crossword puzzle sheets
• Pens or pencils
• Distribute crossword puzzle sheets and pens/pencils to each participant.
• Explain the rules of crossword puzzles and the goal of completing as many clues as possible within the given time.
• Participants individually or in pairs work on solving the crossword puzzle by filling in the correct words.
• Encourage critical thinking, word association, and collaborative discussions for solving challenging clues.
• At the end of the time limit, review the answers and discuss any interesting or challenging clues as a group.
• Enhanced Problem-Solving: Participants engage in critical thinking while deciphering clues, promoting effective problem-solving skills.
• Vocabulary Expansion: Exposure to new words and phrases within the crossword improves vocabulary and comprehension.
• Cognitive Stimulation: The mental exercise of solving the puzzle stimulates the brain, enhancing cognitive abilities.
• Team Collaboration: If done in pairs, participants practice collaboration and communication to solve clues together.
• Achievement and Motivation: Successfully completing the crossword brings a sense of accomplishment and motivates individuals to explore more puzzles.

Tips for Facilitators:

• Provide varying levels of crossword puzzles to accommodate different skill levels.
• Encourage participants to share strategies for solving challenging clues.
• Emphasize the fun and educational aspects of the activity to keep participants engaged.

#2. A Shrinking Vessel

Estimated Time: 10-15 Minutes

• Materials Needed: A rope and a ball of yarn
• Prepare the Setting: Lay a rope on the floor in a shape that allows all team members to stand comfortably inside it. For larger teams, multiple ropes can be used, dividing them into smaller groups.
• Enter the Circle: Have all team members stand inside the rope, ensuring that nobody steps outside its boundaries.
• Shrinking the Circle: Begin gradually shrinking the rope’s size, reducing the available space inside the circle.
• Adapt and Maintain Balance: As the circle shrinks, team members must make subtle adjustments to maintain their positions and balance within the shrinking area.
• The Challenge: The objective for the team is to collectively brainstorm and find innovative ways to keep every team member inside the circle without anyone stepping outside.
• Collaboration and Communication: The activity promotes teamwork and open communication as participants strategize to stay within the shrinking circle.
• Adaptability: Team members learn to adapt swiftly to changing circumstances, fostering agility and flexibility.
• Creative Problem-Solving: The challenge encourages inventive thinking and brainstorming to find unique solutions.
• Trust Building: By relying on each other’s actions, participants build trust and cohesion among team members.
• Time-Efficient: The short duration makes it an ideal icebreaker or energizer during meetings or workshops.
• Observe and Facilitate: Monitor the team’s dynamics and offer guidance to encourage equal participation and effective problem-solving.
• Encourage Verbalization: Prompt participants to voice their ideas and collaborate vocally, aiding in real-time adjustments.
• Debrief Thoughtfully: Engage the team in a discussion afterward, reflecting on strategies employed and lessons learned.
• Emphasize Adaptability: Highlight the transferable skill of adaptability and its significance in both professional and personal contexts.

#3. Human Knots

• Objective: Improving Collaboration & enhancing Communication Skills

Estimated Time: 15-20 minutes

• Materials: None required

Procedure: 

• Organize your team into a compact circle. For more sizable teams, subdivide them into smaller clusters, with each cluster forming its own circle. 
• Direct each individual to grasp the hands of two other people in the circle, with the exception of those positioned directly adjacent to them. This action will result in the formation of a complex “human knot” within the circle. 
• Present the challenge to the group: to unravel themselves from this entanglement while maintaining their hold on each other’s hands. If preferred, you can establish a specific time limit. 
• Observe the team members collaborating to unravel the knot, witnessing their collective effort to devise solutions and free themselves from the intricate puzzle.
• Team Cohesion: The activity encourages team members to interact closely, promoting bonding and understanding among participants.
• Effective Communication: Participants practice clear and concise communication as they coordinate movements to untangle the knot.
• Problem-Solving: The challenge stimulates creative thinking and problem-solving skills as individuals work collectively to find the optimal path for untangling.
• Adaptability: Participants learn to adapt their actions based on the evolving dynamics of the human knot, fostering adaptability.
• Trust Building: As individuals rely on each other to navigate the intricate knot, trust and cooperation naturally develop.
• Set a Positive Tone: Create an inclusive and supportive atmosphere, emphasizing that the focus is on collaboration rather than competition.
• Encourage Verbalization: Urge participants to articulate their intentions and listen to others’ suggestions, promoting effective teamwork.
• Observe Group Dynamics: Monitor interactions and step in if needed to ensure everyone is actively engaged and included.
• Reflect and Share: Conclude the activity with a debriefing session, allowing participants to share their experiences, strategies, and key takeaways.
• Vary Grouping: Change group compositions for subsequent rounds to enhance interactions among different team members.

#4. Egg Drop

Helps With: Decision Making, Collaboration

• A carton of eggs
• Construction materials (balloons, rubber bands, straws, tape, plastic wrap, etc.)
• A suitable location for the activity
• Assign each team a single egg and random construction materials.
• Teams must create a carrier to protect the egg from breaking.
• Drop the carriers one by one and increase the height if necessary to determine the most durable carrier.
• The winning team is the one with the carrier that survives the highest drop.
• Decision Making: Participants engage in critical decision-making processes as they select construction materials and determine carrier designs.
• Collaboration: The activity necessitates collaboration and coordination among team members to construct an effective carrier.
• Problem-Solving: Teams apply creative problem-solving skills to devise innovative methods for safeguarding the egg.
• Risk Management: Participants learn to assess potential risks and consequences while making design choices to prevent egg breakage.
• Celebrating Success: The victorious team experiences a sense of accomplishment, boosting morale and promoting a positive team spirit.
• Provide Diverse Materials: Offer a wide range of construction materials to stimulate creativity and allow teams to explore various design options.
• Set Safety Guidelines: Prioritize safety by specifying a safe drop height and ensuring participants follow safety protocols during construction.
• Encourage Brainstorming: Prompt teams to brainstorm multiple carrier ideas before finalizing their designs, fostering diverse perspectives.
• Facilitate Reflection: After the activity, lead a discussion where teams share their design strategies, challenges faced, and lessons learned.
• Highlight Collaboration: Emphasize the significance of teamwork in achieving success, acknowledging effective communication and cooperation.

#5. Marshmallow Spaghetti Tower

Helps With: Collaboration

Estimated Time: 20-30 Minutes

Materials Needed (per team):

• Raw spaghetti: 20 sticks
• Marshmallow: 1
• String: 1 yard
• Masking tape: 1 roll
• Tower Construction: Instruct teams to collaborate and utilize the provided materials to construct the tallest tower possible within a designated time frame.
• Marshmallow Support: Emphasize that the tower must be capable of standing independently and supporting a marshmallow at its highest point.
• Prototype and Iterate: Encourage teams to engage in prototyping and iteration, testing different design approaches and refining their tower structures.
• T eamwork and Communication: Promote effective teamwork and communication as team members coordinate their efforts to build a stable and tall tower.
• Evaluation Criteria: Evaluate each tower based on its height, stability, and the successful placement of the marshmallow at the top.
• Collaboration: Participants collaborate closely, sharing ideas and working together to design and construct the tower.
• Innovative Thinking: The activity encourages innovative thinking as teams experiment with different strategies to build a stable tower.
• Time Management: Teams practice time management skills as they work within a specified time limit to complete the task.
• Problem-Solving: Participants engage in creative problem-solving to address challenges such as balancing the marshmallow and constructing a sturdy tower.
• Adaptability: Teams adapt their approaches based on trial and error, learning from each iteration to improve their tower designs.
• Set Clear Guidelines: Clearly explain the materials, objectives, and evaluation criteria to ensure teams understand the task.
• Foster Creativity: Encourage teams to think outside the box and explore unconventional methods for constructing their towers.
• Emphasize Collaboration: Highlight the importance of effective communication and teamwork to accomplish the task successfully.
• Time Management: Remind teams of the time limit and encourage them to allocate their time wisely between planning and construction.
• Reflect and Share: Facilitate a discussion after the activity, allowing teams to share their design choices, challenges faced, and lessons learned.

Objective: To engage participants in the strategic and analytical world of Sudoku, enhancing logical thinking and problem-solving abilities. 

Estimated Time: 20-25 Minutes 

• Sudoku puzzle sheets
• Pencils with erasers
• Distribute Sudoku puzzle sheets and pencils to each participant.
• Familiarize participants with the rules and mechanics of Sudoku puzzles.
• Explain the goal: to fill in the empty cells with numbers from 1 to 9 while adhering to the rules of no repetition in rows, columns, or subgrids.
• Encourage participants to analyze the puzzle’s layout, identify potential numbers, and strategically fill in cells.
• Emphasize the importance of logical deduction and step-by-step approach in solving the puzzle.
• Provide hints or guidance if needed, ensuring participants remain engaged and challenged.
• Logical Thinking: Sudoku challenges participants’ logical and deductive reasoning, fostering analytical skills.
• Problem-Solving: The intricate interplay of numbers and constraints hones problem-solving abilities.
• Focus and Patience: Participants practice patience and attention to detail while gradually unveiling the solution.
• Pattern Recognition: Identifying number patterns and possibilities contributes to enhanced pattern recognition skills.
• Personal Achievement: Successfully completing a Sudoku puzzle provides a sense of accomplishment and boosts confidence.
• Offer varying levels of Sudoku puzzles to cater to different skill levels.
• Encourage participants to share strategies and techniques for solving specific challenges.
• Highlight the mental workout Sudoku provides and its transferable skills to real-life problem-solving.

Helps With: Communication, Problem-solving, & Management

• A lockable room
• 5-10 puzzles or clues
• Hide the key and a set of clues around the room.
• Lock the room and provide team members with a specific time limit to find the key and escape.
• Instruct the team to work together, solving the puzzles and deciphering the clues to locate the key.
• Encourage efficient communication and effective problem-solving under time pressure.
• Communication Skills: Participants enhance their communication abilities by sharing observations, ideas, and findings to collectively solve puzzles.
• Problem-solving Proficiency: The activity challenges teams to think critically, apply logical reasoning, and collaboratively tackle intricate challenges.
• Team Management: The experience promotes effective team management as members assign tasks, prioritize efforts, and coordinate actions.
• Time Management: The imposed time limit sharpens time management skills as teams strategize and allocate time wisely.
• Adaptability: Teams learn to adapt and adjust strategies based on progress, evolving clues, and time constraints.
• Clear Introduction: Provide a concise overview of the activity, emphasizing the importance of communication, problem-solving, and time management.
• Diverse Challenges: Offer a mix of puzzles and clues to engage various problem-solving skills, catering to different team strengths.
• Supportive Role: Act as a facilitator, offering subtle guidance if needed while allowing teams to independently explore and solve challenges.
• Debriefing Session: Organize a debriefing session afterward to discuss the experience, highlight successful strategies, and identify areas for improvement.
• Encourage Reflection: Encourage participants to reflect on their teamwork, communication effectiveness, and problem-solving approach.

#8. Frostbite for Group Problem Solving Activities

Helps With: Decision Making, Trust, Leadership

• An electric fan
• Construction materials (toothpicks, cardstock, rubber bands, sticky notes, etc.)
• Divide the team into groups of 4-5 people, each with a designated leader.
• Blindfold team members and prohibit leaders from using their hands.
• Provide teams with construction materials and challenge them to build a tent within 30 minutes.
• Test the tents using the fan to see which can withstand high winds.
• Decision-Making Proficiency: Participants are exposed to critical decision-making situations under constraints, allowing them to practice effective and efficient decision-making.
• Trust Development: Blindfolding team members and relying on the designated leaders fosters trust and collaboration among team members.
• Leadership Skills: Designated leaders navigate the challenge without hands-on involvement, enhancing their leadership and communication skills.
• Creative Problem Solving: Teams employ creative thinking and resourcefulness to construct stable tents with limited sensory input.
• Team Cohesion: The shared task and unique constraints promote team cohesion and mutual understanding.
• Role of the Facilitator: Act as an observer, allowing teams to navigate the challenge with minimal intervention. Offer assistance only when necessary.
• Clarity in Instructions: Provide clear instructions regarding blindfolding, leader restrictions, and time limits to ensure a consistent experience.
• Debriefing Session: After the activity, conduct a debriefing session to discuss team dynamics, leadership approaches, and decision-making strategies.
• Encourage Communication: Emphasize the importance of effective communication within teams to ensure smooth coordination and successful tent construction.
• Acknowledge Creativity: Celebrate creative solutions and innovative approaches exhibited by teams during the tent-building process.

#9. Dumbest Idea First

Helps With: Critical Thinking & Creative Problem Solving Activity

Estimated Time: 15-20 Minutes

Materials Needed: A piece of paper, pen, and pencil

• Problem Presentation: Introduce a specific problem to the team, either a real-world challenge or a hypothetical scenario that requires a solution.
• Brainstorming Dumb Ideas: Instruct team members to quickly generate and jot down the most unconventional and seemingly “dumb” ideas they can think of to address the problem.
• Idea Sharing: Encourage each participant to share their generated ideas with the group, fostering a relaxed and open atmosphere for creative expression.
• Viability Assessment: As a team, review and evaluate each idea, considering potential benefits and drawbacks. Emphasize the goal of identifying unconventional approaches.
• Selecting Promising Solutions: Identify which seemingly “dumb” ideas could hold hidden potential or innovative insights. Discuss how these ideas could be adapted into workable solutions.
• Divergent Thinking: Participants engage in divergent thinking, pushing beyond conventional boundaries to explore unconventional solutions.
• Creative Exploration: The activity sparks creative exploration by encouraging participants to let go of inhibitions and embrace imaginative thinking.
• Critical Analysis: Through evaluating each idea, participants practice critical analysis and learn to identify unique angles and aspects of potential solutions.
• Open Communication: The lighthearted approach of sharing “dumb” ideas fosters open communication, reducing fear of judgment and promoting active participation.
• Solution Adaptation: Identifying elements of seemingly “dumb” ideas that have merit encourages participants to adapt and refine their approaches creatively.
• Safe Environment: Foster a safe and non-judgmental environment where participants feel comfortable sharing unconventional ideas.
• Time Management: Set clear time limits for idea generation and sharing to maintain the activity’s energetic pace.
• Encourage Wild Ideas: Emphasize that the goal is to explore the unconventional, urging participants to push the boundaries of creativity.
• Facilitator Participation: Participate in idea generation to demonstrate an open-minded approach and encourage involvement.
• Debriefing Discussion: After the activity, facilitate a discussion on how seemingly “dumb” ideas can inspire innovative solutions and stimulate fresh thinking.

This activity encourages out-of-the-box thinking and creative problem-solving. It allows teams to explore unconventional ideas that may lead to unexpected, yet effective, solutions.

#10: Legoman

Helps With: Foster teamwork, communication, and creativity through a collaborative Lego-building activity.

Estimated Time: 20-30 minutes

• Lego bricks
• Lego instruction manuals

Procedure :

• Divide participants into small teams of 3-5 members.
• Provide each team with an equal set of Lego bricks and a Lego instruction manual.
• Explain that the goal is for teams to work together to construct the Lego model shown in the manual.
• Set a time limit for the building activity based on model complexity.
• Allow teams to self-organize, build, and collaborate to complete the model within the time limit.
• Evaluate each team’s final model compared to the manual’s original design.
• Enhanced Communication: Participants must communicate clearly and listen actively to collaborate effectively.
• Strengthened Teamwork: Combining efforts toward a shared goal promotes camaraderie and team cohesion.
• Creative Problem-Solving: Teams must creatively problem-solve if pieces are missing or instructions unclear.
• Planning and Resource Allocation: Following instructions fosters planning skills and efficient use of resources.
• Sense of Achievement: Completing a challenging build provides a sense of collective accomplishment.
• Encourage Participation: Urge quieter members to contribute ideas and take an active role.
• Highlight Teamwork: Emphasize how cooperation and task coordination are key to success.
• Ensure Equal Engagement: Monitor group dynamics to ensure all members are engaged.
• Allow Creativity: Permit modifications if teams lack exact pieces or wish to get creative.
• Focus on Enjoyment: Create a lively atmosphere so the activity remains energizing and fun.

#11: Minefield

Helps With: Trust, Communication, Patience

Materials Needed: Open space, blindfolds

• Mark a “minefield” on the ground using ropes, cones, or tape. Add toy mines or paper cups.
• Pair up participants and blindfold one partner.
• Position blindfolded partners at the start of the minefield. Direct seeing partners to verbally guide them through to the other side without hitting “mines.”
• Partners switch roles once finished and repeat.
• Time partnerships and provide prizes for the fastest safe crossing.
• Trust Building: Blindfolded partners must trust their partner’s instructions.
• Effective Communication: Giving clear, specific directions is essential for navigating the minefield.
• Active Listening: Partners must listen closely and follow directions precisely.
• Patience & Support: The exercise requires patience and encouraging guidance between partners.
• Team Coordination: Partners must work in sync, coordinating movements and communication.
• Test Boundaries: Ensure the minefield’s size accommodates safe movement and communication.
• Monitor Interactions: Watch for dominant guidance and ensure both partners participate fully.
• Time Strategically: Adjust time limits based on the minefield size and difficulty.
• Add Obstacles: Introduce additional non-mine objects to increase challenge and communication needs.
• Foster Discussion: Debrief afterward to discuss communication approaches and trust-building takeaways.

#12: Reverse Pyramid

Helps With: Teamwork, Communication, Creativity

Materials Needed: 36 cups per group, tables

• Form small groups of 5-7 participants.
• Provide each group with a stack of 36 cups and a designated building area.
• Explain the objective: Build the tallest pyramid starting with just one cup on top.
• Place the first cup on the table, and anyone in the group can add two cups beneath it to form the second row.
• From this point, only the bottom row can be lifted to add the next row underneath.
• Cups in the pyramid can only be touched or supported by index fingers.
• If the structure falls, start over from one cup.
• Offer more cups if a group uses all provided.
• Allow 15 minutes for building.

Teamwork: Collaborate to construct the pyramid.

Communication: Discuss and execute the building strategy.

Creativity: Find innovative ways to build a tall, stable pyramid.

Clarify Expectations: Emphasize the definition of a pyramid with each row having one less cup.

Encourage Perseverance: Motivate groups to continue despite challenges.

Promote Consensus: Encourage groups to work together and help each other.

Reflect on Failure: Use collapses as a metaphor for overcoming obstacles and improving.

Consider Competitions: Modify the activity for competitive teams and scoring.

#13: Stranded

Helps With: Decision-making, Prioritization, Teamwork

Materials Needed: List of salvaged items, paper, pens

• Present a scenario where teams are stranded and must prioritize items salvaged from a plane crash.
• Provide teams with the same list of ~15 salvaged items.
• Instruct teams to agree on an item ranking with #1 being the most important for survival.
• Teams share and compare their prioritized lists. Identify differences in approach.
• Discuss what factors influenced decisions and how teams worked together to agree on priorities.
• Critical Thinking: Weighing item importance requires analytical thinking and discussion.
• Team Decision-Making: Coming to a consensus fosters team decision-making capabilities.
• Prioritization Skills: Ranking items strengthen prioritization and justification abilities.
• Perspective-Taking: Understanding different prioritizations builds perspective-taking skills.
• Team Cohesion: Collaborating toward a shared goal brings teams closer together.
• Encourage Discussion: Urge teams to discuss all ideas rather than allow single members to dominate.
• Be Engaged: Circulate to listen in on team discussions and pose thought-provoking questions.
• Add Complexity: Introduce scenarios with additional constraints to expand critical thinking.
• Highlight Disagreements: When priorities differ, facilitate constructive discussions on influencing factors.
• Recognize Collaboration: Acknowledge teams that demonstrate exceptional teamwork and communication.

Now let’s look at some common types of problem-solving activities.

Types of Problem-Solving Activities

The most common types of problem-solving activities/exercises are:

• Creative problem-solving activities
• Group problem-solving activities
• Individual problem-solving activities
• Fun problem-solving activities, etc.

In the next segments, we’ll be discussing these types of problem-solving activities in detail. So, keep reading!

Creative Problem-Solving Activities

Creative problem solving (CPS) means using creativity to find new solutions. It involves thinking creatively at first and then evaluating ideas later. For example, think of it like brainstorming fun game ideas, discussing them, and then picking the best one to play.

Some of the most common creative problem-solving activities include:

• Legoman: Building creative structures with LEGO.
• Escape: Solving puzzles to escape a room.
• Frostbite: Finding solutions in challenging situations.
• Minefield: Navigating a field of obstacles.

Group Problem-Solving Activities

Group problem-solving activities are challenges that make teams work together to solve puzzles or overcome obstacles. They enhance teamwork and critical thinking.

For instance, think of a puzzle-solving game where a group must find hidden clues to escape a locked room.

Here are the most common group problem-solving activities you can try in groups:

• A Shrinking Vessel
• Marshmallow Spaghetti Tower
• Cardboard Boat Building Challenge
• Clue Murder Mystery
• Escape Room: Jewel Heist
• Escape Room: Virtual Team Building
• Scavenger Hunt
• Dumbest Idea First

Individual Problem-Solving Activities

As the name suggests, individual problem-solving activities are the tasks that you need to play alone to boost your critical thinking ability. They help you solve problems and stay calm while facing challenges in real life. Like puzzles, they make your brain sharper. Imagine it’s like training your brain muscles to handle tricky situations.

Here are some of the most common individual problem-solving activities:

• Puzzles (jigsaw, crossword, sudoku, etc.)
• Brain teasers
• Logic problems
• Optical illusions
• “Escape room” style games

Fun Problem-Solving Activities

Fun problem-solving activities are enjoyable games that sharpen your critical thinking skills while having a blast. Think of activities like the Legoman challenge, escape rooms, or rolling dice games – they make problem-solving exciting and engaging!

And to be frank, all of the mentioned problem-solving activities are fun if you know how to play and enjoy them as all of them are game-like activities.

Team Problems You Can Address Through Problem Solving Activities

Fun problem-solving activities serve as dynamic tools to address a range of challenges that teams often encounter. These engaging activities foster an environment of collaboration, creativity, and critical thinking, enabling teams to tackle various problems head-on. Here are some common team problems that can be effectively addressed through these activities:

• Communication Breakdowns:  

Activities like “Escape,” “A Shrinking Vessel,” and “Human Knots” emphasize the importance of clear and effective communication. They require teams to work together, exchange ideas, and devise strategies to accomplish a shared goal. By engaging in these activities, team members learn to communicate more efficiently, enhancing overall team communication in real-world situations.

• Lack of Trust and Cohesion:  

Problem-solving activities promote trust and cohesiveness within teams. For instance, “Frostbite” and “Marshmallow Spaghetti Tower” require teams to collaborate closely, trust each other’s ideas, and rely on each member’s strengths. These activities build a sense of unity and trust, which can translate into improved teamwork and collaboration.

• Innovative Thinking:  

“Dumbest Idea First” and “Egg Drop” encourage teams to think outside the box and explore unconventional solutions. These activities challenge teams to be creative and innovative in their problem-solving approaches, fostering a culture of thinking beyond traditional boundaries when faced with complex issues.

• Decision-Making Challenges:  

Activities like “Onethread” facilitate group decision-making by providing a platform for open discussions and collaborative choices. Problem-solving activities require teams to make decisions collectively, teaching them to weigh options, consider different viewpoints, and arrive at informed conclusions—a skill that is transferable to real-world decision-making scenarios.

• Leadership and Role Clarification:  

Activities such as “Frostbite” and “Egg Drop” designate team leaders and roles within groups. This provides an opportunity for team members to practice leadership, delegation, and role-specific tasks. By experiencing leadership dynamics in a controlled setting, teams can improve their leadership skills and better understand their roles in actual projects.

• Problem-Solving Strategies:  

All of the problem-solving activities involve the application of different strategies. Teams learn to analyze problems, break them down into manageable components, and develop systematic approaches for resolution. These strategies can be adapted to real-world challenges, enabling teams to approach complex issues with confidence.

• Team Morale and Engagement:  

Participating in engaging and enjoyable activities boosts team morale and engagement. These activities provide a break from routine tasks, energize team members, and create a positive and fun atmosphere. Elevated team morale can lead to increased motivation and productivity.

The incentives of event prizes can further stimulate the enthusiasm and participation of team members. The choice of prizes is crucial, as it can directly affect the attractiveness and participation of the event. Among them, Medals are essential prizes.

Medals are symbols of honor awarded to winners and represent the value and achievement of an event.

Medals also have a motivational effect, they encourage team members to pursue higher achievements and progress.

Medals are artistic and aesthetic. They are usually designed by designers according to different occasions and themes and have high collection value.

By incorporating these fun problem-solving activities, teams can address a variety of challenges, foster skill development, and build a more cohesive and effective working environment. As teams learn to collaborate, communicate, innovate, and make decisions collectively, they are better equipped to overcome obstacles and achieve shared goals.

The Benefits of Problem Solving Activities for Your Team

#1 Better Thinking

Problem-solving activities bring out the best in team members by encouraging them to contribute their unique ideas. This stimulates better thinking as team managers evaluate different solutions and choose the most suitable ones.

For example, a remote team struggling with communication benefited from quick thinking and the sharing of ideas, leading to the adoption of various communication modes for improved collaboration.

#2 Better Risk Handling

Team building problem solving activities condition individuals to handle risks more effectively. By engaging in challenging situations and finding solutions, team members develop the ability to respond better to stressful circumstances.

#3 Better Communication

Regular communication among team members is crucial for efficient problem-solving. Engaging in problem-solving activities fosters cooperation and communication within the team, resulting in better understanding and collaboration. Using tools like OneThread can further enhance team communication and accountability.

#4 Improved Productivity Output

When teams work cohesively, overall productivity improves, leading to enhanced profit margins for the company or organization. Involving managers and team members in problem-solving activities can positively impact the company’s growth and profitability.

How Onethread Enhances the Effect of Problem Solving Activities

Problem-solving activities within teams thrive on collaborative efforts and shared perspectives. Onethread emerges as a potent facilitator, enabling teams to collectively tackle challenges and harness diverse viewpoints with precision. Here’s a comprehensive view of how Onethread amplifies team collaboration in problem-solving initiatives:

Open Channels for Discussion:

Onethread’s real-time messaging feature serves as a dedicated hub for open and seamless discussions. Teams can engage in brainstorming sessions, share insightful observations, and propose innovative solutions within a flexible environment. Asynchronous communication empowers members to contribute their insights at their convenience, fostering comprehensive problem analysis with ample deliberation.

Centralized Sharing of Resources:

Effective problem-solving often hinges on access to pertinent resources. Onethread’s document sharing functionality ensures that critical information, references, and research findings are centralized and readily accessible. This eradicates the need for cumbersome email attachments and enables team members to collaborate with precise and up-to-date data.

Efficient Task Allocation and Monitoring:

Problem-solving journeys comprise a series of tasks and actions. Onethread’s task management capability streamlines the delegation of specific responsibilities to team members. Assign tasks related to research, data analysis, or solution implementation and monitor progress in real time. This cultivates a sense of accountability and guarantees comprehensive coverage of every facet of the problem-solving process.

Facilitated Collaborative Decision-Making: Navigating intricate problems often demands collective decision-making. Onethread’s collaborative ecosystem empowers teams to deliberate over potential solutions, assess pros and cons, and make well-informed choices. Transparent discussions ensure that decisions are comprehensively comprehended and supported by the entire team.

Seamless Documentation and Insights Sharing:

As the problem-solving journey unfolds, the accumulation of insights and conclusions becomes pivotal. Onethread’s collaborative document editing feature empowers teams to document their discoveries, chronicle the steps undertaken, and showcase successful solutions. This shared repository of documentation serves as a valuable resource for future reference and continuous learning.

With Onethread orchestrating the backdrop, team collaboration during problem-solving activities transforms into a harmonious fusion of insights, ideas, and actionable steps.

What are the 5 problem-solving skills?

The top 5 problem-solving skills in 2023 are critical thinking, creativity, emotional intelligence, adaptability, and data literacy. Most employers seek these skills in their workforce.

What are the steps of problem-solving?

Problem-solving steps are as follows: 1. Define the problem clearly. 2. Analyze the issue in detail. 3. Generate potential solutions. 4. Evaluate these options. 5. Choose the best solution. 6. Put the chosen solution into action. 7. Measure the outcomes to assess effectiveness and improvements made. These sequential steps assist in efficient and effective problem resolution.

How do you teach problem-solving skills?

Teaching problem-solving involves modelling effective methods within a context, helping students grasp the problem, dedicating ample time, asking guiding questions, and giving suggestions. Connect errors to misconceptions to enhance understanding, fostering a straightforward approach to building problem-solving skills.

So here is all about “activities for problem solving”.No matter which activity you choose, engaging in problem-solving activities not only provides entertainment but also helps enhance cognitive abilities such as critical thinking, decision making, and creativity. So why not make problem solving a regular part of your routine?

Take some time each day or week to engage in these activities and watch as your problem-solving skills grow stronger. Plus, it’s an enjoyable way to pass the time and challenge yourself mentally.

So go ahead, grab a puzzle or gather some friends for a game night – get ready to have fun while sharpening your problem-solving skills!

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7 Module 7: Thinking, Reasoning, and Problem-Solving

This module is about how a solid working knowledge of psychological principles can help you to think more effectively, so you can succeed in school and life. You might be inclined to believe that—because you have been thinking for as long as you can remember, because you are able to figure out the solution to many problems, because you feel capable of using logic to argue a point, because you can evaluate whether the things you read and hear make sense—you do not need any special training in thinking. But this, of course, is one of the key barriers to helping people think better. If you do not believe that there is anything wrong, why try to fix it?

The human brain is indeed a remarkable thinking machine, capable of amazing, complex, creative, logical thoughts. Why, then, are we telling you that you need to learn how to think? Mainly because one major lesson from cognitive psychology is that these capabilities of the human brain are relatively infrequently realized. Many psychologists believe that people are essentially “cognitive misers.” It is not that we are lazy, but that we have a tendency to expend the least amount of mental effort necessary. Although you may not realize it, it actually takes a great deal of energy to think. Careful, deliberative reasoning and critical thinking are very difficult. Because we seem to be successful without going to the trouble of using these skills well, it feels unnecessary to develop them. As you shall see, however, there are many pitfalls in the cognitive processes described in this module. When people do not devote extra effort to learning and improving reasoning, problem solving, and critical thinking skills, they make many errors.

As is true for memory, if you develop the cognitive skills presented in this module, you will be more successful in school. It is important that you realize, however, that these skills will help you far beyond school, even more so than a good memory will. Although it is somewhat useful to have a good memory, ten years from now no potential employer will care how many questions you got right on multiple choice exams during college. All of them will, however, recognize whether you are a logical, analytical, critical thinker. With these thinking skills, you will be an effective, persuasive communicator and an excellent problem solver.

The module begins by describing different kinds of thought and knowledge, especially conceptual knowledge and critical thinking. An understanding of these differences will be valuable as you progress through school and encounter different assignments that require you to tap into different kinds of knowledge. The second section covers deductive and inductive reasoning, which are processes we use to construct and evaluate strong arguments. They are essential skills to have whenever you are trying to persuade someone (including yourself) of some point, or to respond to someone’s efforts to persuade you. The module ends with a section about problem solving. A solid understanding of the key processes involved in problem solving will help you to handle many daily challenges.

7.1. Different kinds of thought

7.2. Reasoning and Judgment

7.3. Problem Solving

READING WITH PURPOSE

Remember and understand.

By reading and studying Module 7, you should be able to remember and describe:

• Concepts and inferences (7.1)
• Procedural knowledge (7.1)
• Metacognition (7.1)
• Characteristics of critical thinking:  skepticism; identify biases, distortions, omissions, and assumptions; reasoning and problem solving skills  (7.1)
• Reasoning:  deductive reasoning, deductively valid argument, inductive reasoning, inductively strong argument, availability heuristic, representativeness heuristic  (7.2)
• Fixation:  functional fixedness, mental set  (7.3)
• Algorithms, heuristics, and the role of confirmation bias (7.3)
• Effective problem solving sequence (7.3)

By reading and thinking about how the concepts in Module 6 apply to real life, you should be able to:

• Identify which type of knowledge a piece of information is (7.1)
• Recognize examples of deductive and inductive reasoning (7.2)
• Recognize judgments that have probably been influenced by the availability heuristic (7.2)
• Recognize examples of problem solving heuristics and algorithms (7.3)

Analyze, Evaluate, and Create

By reading and thinking about Module 6, participating in classroom activities, and completing out-of-class assignments, you should be able to:

• Use the principles of critical thinking to evaluate information (7.1)
• Explain whether examples of reasoning arguments are deductively valid or inductively strong (7.2)
• Outline how you could try to solve a problem from your life using the effective problem solving sequence (7.3)

7.1. Different kinds of thought and knowledge

• Take a few minutes to write down everything that you know about dogs.
• Do you believe that:
• Psychic ability exists?
• Hypnosis is an altered state of consciousness?
• Magnet therapy is effective for relieving pain?
• Aerobic exercise is an effective treatment for depression?
• UFO’s from outer space have visited earth?

On what do you base your belief or disbelief for the questions above?

Of course, we all know what is meant by the words  think  and  knowledge . You probably also realize that they are not unitary concepts; there are different kinds of thought and knowledge. In this section, let us look at some of these differences. If you are familiar with these different kinds of thought and pay attention to them in your classes, it will help you to focus on the right goals, learn more effectively, and succeed in school. Different assignments and requirements in school call on you to use different kinds of knowledge or thought, so it will be very helpful for you to learn to recognize them (Anderson, et al. 2001).

Factual and conceptual knowledge

Module 5 introduced the idea of declarative memory, which is composed of facts and episodes. If you have ever played a trivia game or watched Jeopardy on TV, you realize that the human brain is able to hold an extraordinary number of facts. Likewise, you realize that each of us has an enormous store of episodes, essentially facts about events that happened in our own lives. It may be difficult to keep that in mind when we are struggling to retrieve one of those facts while taking an exam, however. Part of the problem is that, in contradiction to the advice from Module 5, many students continue to try to memorize course material as a series of unrelated facts (picture a history student simply trying to memorize history as a set of unrelated dates without any coherent story tying them together). Facts in the real world are not random and unorganized, however. It is the way that they are organized that constitutes a second key kind of knowledge, conceptual.

Concepts are nothing more than our mental representations of categories of things in the world. For example, think about dogs. When you do this, you might remember specific facts about dogs, such as they have fur and they bark. You may also recall dogs that you have encountered and picture them in your mind. All of this information (and more) makes up your concept of dog. You can have concepts of simple categories (e.g., triangle), complex categories (e.g., small dogs that sleep all day, eat out of the garbage, and bark at leaves), kinds of people (e.g., psychology professors), events (e.g., birthday parties), and abstract ideas (e.g., justice). Gregory Murphy (2002) refers to concepts as the “glue that holds our mental life together” (p. 1). Very simply, summarizing the world by using concepts is one of the most important cognitive tasks that we do. Our conceptual knowledge  is  our knowledge about the world. Individual concepts are related to each other to form a rich interconnected network of knowledge. For example, think about how the following concepts might be related to each other: dog, pet, play, Frisbee, chew toy, shoe. Or, of more obvious use to you now, how these concepts are related: working memory, long-term memory, declarative memory, procedural memory, and rehearsal? Because our minds have a natural tendency to organize information conceptually, when students try to remember course material as isolated facts, they are working against their strengths.

One last important point about concepts is that they allow you to instantly know a great deal of information about something. For example, if someone hands you a small red object and says, “here is an apple,” they do not have to tell you, “it is something you can eat.” You already know that you can eat it because it is true by virtue of the fact that the object is an apple; this is called drawing an  inference , assuming that something is true on the basis of your previous knowledge (for example, of category membership or of how the world works) or logical reasoning.

Procedural knowledge

Physical skills, such as tying your shoes, doing a cartwheel, and driving a car (or doing all three at the same time, but don’t try this at home) are certainly a kind of knowledge. They are procedural knowledge, the same idea as procedural memory that you saw in Module 5. Mental skills, such as reading, debating, and planning a psychology experiment, are procedural knowledge, as well. In short, procedural knowledge is the knowledge how to do something (Cohen & Eichenbaum, 1993).

Metacognitive knowledge

Floyd used to think that he had a great memory. Now, he has a better memory. Why? Because he finally realized that his memory was not as great as he once thought it was. Because Floyd eventually learned that he often forgets where he put things, he finally developed the habit of putting things in the same place. (Unfortunately, he did not learn this lesson before losing at least 5 watches and a wedding ring.) Because he finally realized that he often forgets to do things, he finally started using the To Do list app on his phone. And so on. Floyd’s insights about the real limitations of his memory have allowed him to remember things that he used to forget.

All of us have knowledge about the way our own minds work. You may know that you have a good memory for people’s names and a poor memory for math formulas. Someone else might realize that they have difficulty remembering to do things, like stopping at the store on the way home. Others still know that they tend to overlook details. This knowledge about our own thinking is actually quite important; it is called metacognitive knowledge, or  metacognition . Like other kinds of thinking skills, it is subject to error. For example, in unpublished research, one of the authors surveyed about 120 General Psychology students on the first day of the term. Among other questions, the students were asked them to predict their grade in the class and report their current Grade Point Average. Two-thirds of the students predicted that their grade in the course would be higher than their GPA. (The reality is that at our college, students tend to earn lower grades in psychology than their overall GPA.) Another example: Students routinely report that they thought they had done well on an exam, only to discover, to their dismay, that they were wrong (more on that important problem in a moment). Both errors reveal a breakdown in metacognition.

The Dunning-Kruger Effect

In general, most college students probably do not study enough. For example, using data from the National Survey of Student Engagement, Fosnacht, McCormack, and Lerma (2018) reported that first-year students at 4-year colleges in the U.S. averaged less than 14 hours per week preparing for classes. The typical suggestion is that you should spend two hours outside of class for every hour in class, or 24 – 30 hours per week for a full-time student. Clearly, students in general are nowhere near that recommended mark. Many observers, including some faculty, believe that this shortfall is a result of students being too busy or lazy. Now, it may be true that many students are too busy, with work and family obligations, for example. Others, are not particularly motivated in school, and therefore might correctly be labeled lazy. A third possible explanation, however, is that some students might not think they need to spend this much time. And this is a matter of metacognition. Consider the scenario that we mentioned above, students thinking they had done well on an exam only to discover that they did not. Justin Kruger and David Dunning examined scenarios very much like this in 1999. Kruger and Dunning gave research participants tests measuring humor, logic, and grammar. Then, they asked the participants to assess their own abilities and test performance in these areas. They found that participants in general tended to overestimate their abilities, already a problem with metacognition. Importantly, the participants who scored the lowest overestimated their abilities the most. Specifically, students who scored in the bottom quarter (averaging in the 12th percentile) thought they had scored in the 62nd percentile. This has become known as the  Dunning-Kruger effect . Many individual faculty members have replicated these results with their own student on their course exams, including the authors of this book. Think about it. Some students who just took an exam and performed poorly believe that they did well before seeing their score. It seems very likely that these are the very same students who stopped studying the night before because they thought they were “done.” Quite simply, it is not just that they did not know the material. They did not know that they did not know the material. That is poor metacognition.

In order to develop good metacognitive skills, you should continually monitor your thinking and seek frequent feedback on the accuracy of your thinking (Medina, Castleberry, & Persky 2017). For example, in classes get in the habit of predicting your exam grades. As soon as possible after taking an exam, try to find out which questions you missed and try to figure out why. If you do this soon enough, you may be able to recall the way it felt when you originally answered the question. Did you feel confident that you had answered the question correctly? Then you have just discovered an opportunity to improve your metacognition. Be on the lookout for that feeling and respond with caution.

concept :  a mental representation of a category of things in the world

Dunning-Kruger effect : individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

inference : an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

metacognition :  knowledge about one’s own cognitive processes; thinking about your thinking

Critical thinking

One particular kind of knowledge or thinking skill that is related to metacognition is  critical thinking (Chew, 2020). You may have noticed that critical thinking is an objective in many college courses, and thus it could be a legitimate topic to cover in nearly any college course. It is particularly appropriate in psychology, however. As the science of (behavior and) mental processes, psychology is obviously well suited to be the discipline through which you should be introduced to this important way of thinking.

More importantly, there is a particular need to use critical thinking in psychology. We are all, in a way, experts in human behavior and mental processes, having engaged in them literally since birth. Thus, perhaps more than in any other class, students typically approach psychology with very clear ideas and opinions about its subject matter. That is, students already “know” a lot about psychology. The problem is, “it ain’t so much the things we don’t know that get us into trouble. It’s the things we know that just ain’t so” (Ward, quoted in Gilovich 1991). Indeed, many of students’ preconceptions about psychology are just plain wrong. Randolph Smith (2002) wrote a book about critical thinking in psychology called  Challenging Your Preconceptions,  highlighting this fact. On the other hand, many of students’ preconceptions about psychology are just plain right! But wait, how do you know which of your preconceptions are right and which are wrong? And when you come across a research finding or theory in this class that contradicts your preconceptions, what will you do? Will you stick to your original idea, discounting the information from the class? Will you immediately change your mind? Critical thinking can help us sort through this confusing mess.

But what is critical thinking? The goal of critical thinking is simple to state (but extraordinarily difficult to achieve): it is to be right, to draw the correct conclusions, to believe in things that are true and to disbelieve things that are false. We will provide two definitions of critical thinking (or, if you like, one large definition with two distinct parts). First, a more conceptual one: Critical thinking is thinking like a scientist in your everyday life (Schmaltz, Jansen, & Wenckowski, 2017).  Our second definition is more operational; it is simply a list of skills that are essential to be a critical thinker. Critical thinking entails solid reasoning and problem solving skills; skepticism; and an ability to identify biases, distortions, omissions, and assumptions. Excellent deductive and inductive reasoning, and problem solving skills contribute to critical thinking. So, you can consider the subject matter of sections 7.2 and 7.3 to be part of critical thinking. Because we will be devoting considerable time to these concepts in the rest of the module, let us begin with a discussion about the other aspects of critical thinking.

Let’s address that first part of the definition. Scientists form hypotheses, or predictions about some possible future observations. Then, they collect data, or information (think of this as making those future observations). They do their best to make unbiased observations using reliable techniques that have been verified by others. Then, and only then, they draw a conclusion about what those observations mean. Oh, and do not forget the most important part. “Conclusion” is probably not the most appropriate word because this conclusion is only tentative. A scientist is always prepared that someone else might come along and produce new observations that would require a new conclusion be drawn. Wow! If you like to be right, you could do a lot worse than using a process like this.

A Critical Thinker’s Toolkit 

Now for the second part of the definition. Good critical thinkers (and scientists) rely on a variety of tools to evaluate information. Perhaps the most recognizable tool for critical thinking is  skepticism (and this term provides the clearest link to the thinking like a scientist definition, as you are about to see). Some people intend it as an insult when they call someone a skeptic. But if someone calls you a skeptic, if they are using the term correctly, you should consider it a great compliment. Simply put, skepticism is a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided. People from Missouri should recognize this principle, as Missouri is known as the Show-Me State. As a skeptic, you are not inclined to believe something just because someone said so, because someone else believes it, or because it sounds reasonable. You must be persuaded by high quality evidence.

Of course, if that evidence is produced, you have a responsibility as a skeptic to change your belief. Failure to change a belief in the face of good evidence is not skepticism; skepticism has open mindedness at its core. M. Neil Browne and Stuart Keeley (2018) use the term weak sense critical thinking to describe critical thinking behaviors that are used only to strengthen a prior belief. Strong sense critical thinking, on the other hand, has as its goal reaching the best conclusion. Sometimes that means strengthening your prior belief, but sometimes it means changing your belief to accommodate the better evidence.

Many times, a failure to think critically or weak sense critical thinking is related to a  bias , an inclination, tendency, leaning, or prejudice. Everybody has biases, but many people are unaware of them. Awareness of your own biases gives you the opportunity to control or counteract them. Unfortunately, however, many people are happy to let their biases creep into their attempts to persuade others; indeed, it is a key part of their persuasive strategy. To see how these biases influence messages, just look at the different descriptions and explanations of the same events given by people of different ages or income brackets, or conservative versus liberal commentators, or by commentators from different parts of the world. Of course, to be successful, these people who are consciously using their biases must disguise them. Even undisguised biases can be difficult to identify, so disguised ones can be nearly impossible.

Here are some common sources of biases:

• Personal values and beliefs.  Some people believe that human beings are basically driven to seek power and that they are typically in competition with one another over scarce resources. These beliefs are similar to the world-view that political scientists call “realism.” Other people believe that human beings prefer to cooperate and that, given the chance, they will do so. These beliefs are similar to the world-view known as “idealism.” For many people, these deeply held beliefs can influence, or bias, their interpretations of such wide ranging situations as the behavior of nations and their leaders or the behavior of the driver in the car ahead of you. For example, if your worldview is that people are typically in competition and someone cuts you off on the highway, you may assume that the driver did it purposely to get ahead of you. Other types of beliefs about the way the world is or the way the world should be, for example, political beliefs, can similarly become a significant source of bias.
• Racism, sexism, ageism and other forms of prejudice and bigotry.  These are, sadly, a common source of bias in many people. They are essentially a special kind of “belief about the way the world is.” These beliefs—for example, that women do not make effective leaders—lead people to ignore contradictory evidence (examples of effective women leaders, or research that disputes the belief) and to interpret ambiguous evidence in a way consistent with the belief.
• Self-interest.  When particular people benefit from things turning out a certain way, they can sometimes be very susceptible to letting that interest bias them. For example, a company that will earn a profit if they sell their product may have a bias in the way that they give information about their product. A union that will benefit if its members get a generous contract might have a bias in the way it presents information about salaries at competing organizations. (Note that our inclusion of examples describing both companies and unions is an explicit attempt to control for our own personal biases). Home buyers are often dismayed to discover that they purchased their dream house from someone whose self-interest led them to lie about flooding problems in the basement or back yard. This principle, the biasing power of self-interest, is likely what led to the famous phrase  Caveat Emptor  (let the buyer beware) .  

Knowing that these types of biases exist will help you evaluate evidence more critically. Do not forget, though, that people are not always keen to let you discover the sources of biases in their arguments. For example, companies or political organizations can sometimes disguise their support of a research study by contracting with a university professor, who comes complete with a seemingly unbiased institutional affiliation, to conduct the study.

People’s biases, conscious or unconscious, can lead them to make omissions, distortions, and assumptions that undermine our ability to correctly evaluate evidence. It is essential that you look for these elements. Always ask, what is missing, what is not as it appears, and what is being assumed here? For example, consider this (fictional) chart from an ad reporting customer satisfaction at 4 local health clubs.

Clearly, from the results of the chart, one would be tempted to give Club C a try, as customer satisfaction is much higher than for the other 3 clubs.

There are so many distortions and omissions in this chart, however, that it is actually quite meaningless. First, how was satisfaction measured? Do the bars represent responses to a survey? If so, how were the questions asked? Most importantly, where is the missing scale for the chart? Although the differences look quite large, are they really?

Well, here is the same chart, with a different scale, this time labeled:

Club C is not so impressive any more, is it? In fact, all of the health clubs have customer satisfaction ratings (whatever that means) between 85% and 88%. In the first chart, the entire scale of the graph included only the percentages between 83 and 89. This “judicious” choice of scale—some would call it a distortion—and omission of that scale from the chart make the tiny differences among the clubs seem important, however.

Also, in order to be a critical thinker, you need to learn to pay attention to the assumptions that underlie a message. Let us briefly illustrate the role of assumptions by touching on some people’s beliefs about the criminal justice system in the US. Some believe that a major problem with our judicial system is that many criminals go free because of legal technicalities. Others believe that a major problem is that many innocent people are convicted of crimes. The simple fact is, both types of errors occur. A person’s conclusion about which flaw in our judicial system is the greater tragedy is based on an assumption about which of these is the more serious error (letting the guilty go free or convicting the innocent). This type of assumption is called a value assumption (Browne and Keeley, 2018). It reflects the differences in values that people develop, differences that may lead us to disregard valid evidence that does not fit in with our particular values.

Oh, by the way, some students probably noticed this, but the seven tips for evaluating information that we shared in Module 1 are related to this. Actually, they are part of this section. The tips are, to a very large degree, set of ideas you can use to help you identify biases, distortions, omissions, and assumptions. If you do not remember this section, we strongly recommend you take a few minutes to review it.

skepticism :  a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

bias : an inclination, tendency, leaning, or prejudice

• Which of your beliefs (or disbeliefs) from the Activate exercise for this section were derived from a process of critical thinking? If some of your beliefs were not based on critical thinking, are you willing to reassess these beliefs? If the answer is no, why do you think that is? If the answer is yes, what concrete steps will you take?

7.2 Reasoning and Judgment

• What percentage of kidnappings are committed by strangers?
• Which area of the house is riskiest: kitchen, bathroom, or stairs?
• What is the most common cancer in the US?
• What percentage of workplace homicides are committed by co-workers?

An essential set of procedural thinking skills is  reasoning , the ability to generate and evaluate solid conclusions from a set of statements or evidence. You should note that these conclusions (when they are generated instead of being evaluated) are one key type of inference that we described in Section 7.1. There are two main types of reasoning, deductive and inductive.

Deductive reasoning

Suppose your teacher tells you that if you get an A on the final exam in a course, you will get an A for the whole course. Then, you get an A on the final exam. What will your final course grade be? Most people can see instantly that you can conclude with certainty that you will get an A for the course. This is a type of reasoning called  deductive reasoning , which is defined as reasoning in which a conclusion is guaranteed to be true as long as the statements leading to it are true. The three statements can be listed as an  argument , with two beginning statements and a conclusion:

Statement 1: If you get an A on the final exam, you will get an A for the course

Statement 2: You get an A on the final exam

Conclusion: You will get an A for the course

This particular arrangement, in which true beginning statements lead to a guaranteed true conclusion, is known as a  deductively valid argument . Although deductive reasoning is often the subject of abstract, brain-teasing, puzzle-like word problems, it is actually an extremely important type of everyday reasoning. It is just hard to recognize sometimes. For example, imagine that you are looking for your car keys and you realize that they are either in the kitchen drawer or in your book bag. After looking in the kitchen drawer, you instantly know that they must be in your book bag. That conclusion results from a simple deductive reasoning argument. In addition, solid deductive reasoning skills are necessary for you to succeed in the sciences, philosophy, math, computer programming, and any endeavor involving the use of logic to persuade others to your point of view or to evaluate others’ arguments.

Cognitive psychologists, and before them philosophers, have been quite interested in deductive reasoning, not so much for its practical applications, but for the insights it can offer them about the ways that human beings think. One of the early ideas to emerge from the examination of deductive reasoning is that people learn (or develop) mental versions of rules that allow them to solve these types of reasoning problems (Braine, 1978; Braine, Reiser, & Rumain, 1984). The best way to see this point of view is to realize that there are different possible rules, and some of them are very simple. For example, consider this rule of logic:

therefore q

Logical rules are often presented abstractly, as letters, in order to imply that they can be used in very many specific situations. Here is a concrete version of the of the same rule:

I’ll either have pizza or a hamburger for dinner tonight (p or q)

I won’t have pizza (not p)

Therefore, I’ll have a hamburger (therefore q)

This kind of reasoning seems so natural, so easy, that it is quite plausible that we would use a version of this rule in our daily lives. At least, it seems more plausible than some of the alternative possibilities—for example, that we need to have experience with the specific situation (pizza or hamburger, in this case) in order to solve this type of problem easily. So perhaps there is a form of natural logic (Rips, 1990) that contains very simple versions of logical rules. When we are faced with a reasoning problem that maps onto one of these rules, we use the rule.

But be very careful; things are not always as easy as they seem. Even these simple rules are not so simple. For example, consider the following rule. Many people fail to realize that this rule is just as valid as the pizza or hamburger rule above.

if p, then q

therefore, not p

Concrete version:

If I eat dinner, then I will have dessert

I did not have dessert

Therefore, I did not eat dinner

The simple fact is, it can be very difficult for people to apply rules of deductive logic correctly; as a result, they make many errors when trying to do so. Is this a deductively valid argument or not?

Students who like school study a lot

Students who study a lot get good grades

Jane does not like school

Therefore, Jane does not get good grades

Many people are surprised to discover that this is not a logically valid argument; the conclusion is not guaranteed to be true from the beginning statements. Although the first statement says that students who like school study a lot, it does NOT say that students who do not like school do not study a lot. In other words, it may very well be possible to study a lot without liking school. Even people who sometimes get problems like this right might not be using the rules of deductive reasoning. Instead, they might just be making judgments for examples they know, in this case, remembering instances of people who get good grades despite not liking school.

Making deductive reasoning even more difficult is the fact that there are two important properties that an argument may have. One, it can be valid or invalid (meaning that the conclusion does or does not follow logically from the statements leading up to it). Two, an argument (or more correctly, its conclusion) can be true or false. Here is an example of an argument that is logically valid, but has a false conclusion (at least we think it is false).

Either you are eleven feet tall or the Grand Canyon was created by a spaceship crashing into the earth.

You are not eleven feet tall

Therefore the Grand Canyon was created by a spaceship crashing into the earth

This argument has the exact same form as the pizza or hamburger argument above, making it is deductively valid. The conclusion is so false, however, that it is absurd (of course, the reason the conclusion is false is that the first statement is false). When people are judging arguments, they tend to not observe the difference between deductive validity and the empirical truth of statements or conclusions. If the elements of an argument happen to be true, people are likely to judge the argument logically valid; if the elements are false, they will very likely judge it invalid (Markovits & Bouffard-Bouchard, 1992; Moshman & Franks, 1986). Thus, it seems a stretch to say that people are using these logical rules to judge the validity of arguments. Many psychologists believe that most people actually have very limited deductive reasoning skills (Johnson-Laird, 1999). They argue that when faced with a problem for which deductive logic is required, people resort to some simpler technique, such as matching terms that appear in the statements and the conclusion (Evans, 1982). This might not seem like a problem, but what if reasoners believe that the elements are true and they happen to be wrong; they will would believe that they are using a form of reasoning that guarantees they are correct and yet be wrong.

deductive reasoning :  a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

argument :  a set of statements in which the beginning statements lead to a conclusion

deductively valid argument :  an argument for which true beginning statements guarantee that the conclusion is true

Inductive reasoning and judgment

Every day, you make many judgments about the likelihood of one thing or another. Whether you realize it or not, you are practicing  inductive reasoning   on a daily basis. In inductive reasoning arguments, a conclusion is likely whenever the statements preceding it are true. The first thing to notice about inductive reasoning is that, by definition, you can never be sure about your conclusion; you can only estimate how likely the conclusion is. Inductive reasoning may lead you to focus on Memory Encoding and Recoding when you study for the exam, but it is possible the instructor will ask more questions about Memory Retrieval instead. Unlike deductive reasoning, the conclusions you reach through inductive reasoning are only probable, not certain. That is why scientists consider inductive reasoning weaker than deductive reasoning. But imagine how hard it would be for us to function if we could not act unless we were certain about the outcome.

Inductive reasoning can be represented as logical arguments consisting of statements and a conclusion, just as deductive reasoning can be. In an inductive argument, you are given some statements and a conclusion (or you are given some statements and must draw a conclusion). An argument is  inductively strong   if the conclusion would be very probable whenever the statements are true. So, for example, here is an inductively strong argument:

• Statement #1: The forecaster on Channel 2 said it is going to rain today.
• Statement #2: The forecaster on Channel 5 said it is going to rain today.
• Statement #3: It is very cloudy and humid.
• Statement #4: You just heard thunder.
• Conclusion (or judgment): It is going to rain today.

Think of the statements as evidence, on the basis of which you will draw a conclusion. So, based on the evidence presented in the four statements, it is very likely that it will rain today. Will it definitely rain today? Certainly not. We can all think of times that the weather forecaster was wrong.

A true story: Some years ago psychology student was watching a baseball playoff game between the St. Louis Cardinals and the Los Angeles Dodgers. A graphic on the screen had just informed the audience that the Cardinal at bat, (Hall of Fame shortstop) Ozzie Smith, a switch hitter batting left-handed for this plate appearance, had never, in nearly 3000 career at-bats, hit a home run left-handed. The student, who had just learned about inductive reasoning in his psychology class, turned to his companion (a Cardinals fan) and smugly said, “It is an inductively strong argument that Ozzie Smith will not hit a home run.” He turned back to face the television just in time to watch the ball sail over the right field fence for a home run. Although the student felt foolish at the time, he was not wrong. It was an inductively strong argument; 3000 at-bats is an awful lot of evidence suggesting that the Wizard of Ozz (as he was known) would not be hitting one out of the park (think of each at-bat without a home run as a statement in an inductive argument). Sadly (for the die-hard Cubs fan and Cardinals-hating student), despite the strength of the argument, the conclusion was wrong.

Given the possibility that we might draw an incorrect conclusion even with an inductively strong argument, we really want to be sure that we do, in fact, make inductively strong arguments. If we judge something probable, it had better be probable. If we judge something nearly impossible, it had better not happen. Think of inductive reasoning, then, as making reasonably accurate judgments of the probability of some conclusion given a set of evidence.

We base many decisions in our lives on inductive reasoning. For example:

Statement #1: Psychology is not my best subject

Statement #2: My psychology instructor has a reputation for giving difficult exams

Statement #3: My first psychology exam was much harder than I expected

Judgment: The next exam will probably be very difficult.

Decision: I will study tonight instead of watching Netflix.

Some other examples of judgments that people commonly make in a school context include judgments of the likelihood that:

• A particular class will be interesting/useful/difficult
• You will be able to finish writing a paper by next week if you go out tonight
• Your laptop’s battery will last through the next trip to the library
• You will not miss anything important if you skip class tomorrow
• Your instructor will not notice if you skip class tomorrow
• You will be able to find a book that you will need for a paper
• There will be an essay question about Memory Encoding on the next exam

Tversky and Kahneman (1983) recognized that there are two general ways that we might make these judgments; they termed them extensional (i.e., following the laws of probability) and intuitive (i.e., using shortcuts or heuristics, see below). We will use a similar distinction between Type 1 and Type 2 thinking, as described by Keith Stanovich and his colleagues (Evans and Stanovich, 2013; Stanovich and West, 2000). Type 1 thinking is fast, automatic, effortful, and emotional. In fact, it is hardly fair to call it reasoning at all, as judgments just seem to pop into one’s head. Type 2 thinking , on the other hand, is slow, effortful, and logical. So obviously, it is more likely to lead to a correct judgment, or an optimal decision. The problem is, we tend to over-rely on Type 1. Now, we are not saying that Type 2 is the right way to go for every decision or judgment we make. It seems a bit much, for example, to engage in a step-by-step logical reasoning procedure to decide whether we will have chicken or fish for dinner tonight.

Many bad decisions in some very important contexts, however, can be traced back to poor judgments of the likelihood of certain risks or outcomes that result from the use of Type 1 when a more logical reasoning process would have been more appropriate. For example:

Statement #1: It is late at night.

Statement #2: Albert has been drinking beer for the past five hours at a party.

Statement #3: Albert is not exactly sure where he is or how far away home is.

Judgment: Albert will have no difficulty walking home.

Decision: He walks home alone.

As you can see in this example, the three statements backing up the judgment do not really support it. In other words, this argument is not inductively strong because it is based on judgments that ignore the laws of probability. What are the chances that someone facing these conditions will be able to walk home alone easily? And one need not be drunk to make poor decisions based on judgments that just pop into our heads.

The truth is that many of our probability judgments do not come very close to what the laws of probability say they should be. Think about it. In order for us to reason in accordance with these laws, we would need to know the laws of probability, which would allow us to calculate the relationship between particular pieces of evidence and the probability of some outcome (i.e., how much likelihood should change given a piece of evidence), and we would have to do these heavy math calculations in our heads. After all, that is what Type 2 requires. Needless to say, even if we were motivated, we often do not even know how to apply Type 2 reasoning in many cases.

So what do we do when we don’t have the knowledge, skills, or time required to make the correct mathematical judgment? Do we hold off and wait until we can get better evidence? Do we read up on probability and fire up our calculator app so we can compute the correct probability? Of course not. We rely on Type 1 thinking. We “wing it.” That is, we come up with a likelihood estimate using some means at our disposal. Psychologists use the term heuristic to describe the type of “winging it” we are talking about. A  heuristic   is a shortcut strategy that we use to make some judgment or solve some problem (see Section 7.3). Heuristics are easy and quick, think of them as the basic procedures that are characteristic of Type 1.  They can absolutely lead to reasonably good judgments and decisions in some situations (like choosing between chicken and fish for dinner). They are, however, far from foolproof. There are, in fact, quite a lot of situations in which heuristics can lead us to make incorrect judgments, and in many cases the decisions based on those judgments can have serious consequences.

Let us return to the activity that begins this section. You were asked to judge the likelihood (or frequency) of certain events and risks. You were free to come up with your own evidence (or statements) to make these judgments. This is where a heuristic crops up. As a judgment shortcut, we tend to generate specific examples of those very events to help us decide their likelihood or frequency. For example, if we are asked to judge how common, frequent, or likely a particular type of cancer is, many of our statements would be examples of specific cancer cases:

Statement #1: Andy Kaufman (comedian) had lung cancer.

Statement #2: Colin Powell (US Secretary of State) had prostate cancer.

Statement #3: Bob Marley (musician) had skin and brain cancer

Statement #4: Sandra Day O’Connor (Supreme Court Justice) had breast cancer.

Statement #5: Fred Rogers (children’s entertainer) had stomach cancer.

Statement #6: Robin Roberts (news anchor) had breast cancer.

Statement #7: Bette Davis (actress) had breast cancer.

Judgment: Breast cancer is the most common type.

Your own experience or memory may also tell you that breast cancer is the most common type. But it is not (although it is common). Actually, skin cancer is the most common type in the US. We make the same types of misjudgments all the time because we do not generate the examples or evidence according to their actual frequencies or probabilities. Instead, we have a tendency (or bias) to search for the examples in memory; if they are easy to retrieve, we assume that they are common. To rephrase this in the language of the heuristic, events seem more likely to the extent that they are available to memory. This bias has been termed the  availability heuristic   (Kahneman and Tversky, 1974).

The fact that we use the availability heuristic does not automatically mean that our judgment is wrong. The reason we use heuristics in the first place is that they work fairly well in many cases (and, of course that they are easy to use). So, the easiest examples to think of sometimes are the most common ones. Is it more likely that a member of the U.S. Senate is a man or a woman? Most people have a much easier time generating examples of male senators. And as it turns out, the U.S. Senate has many more men than women (74 to 26 in 2020). In this case, then, the availability heuristic would lead you to make the correct judgment; it is far more likely that a senator would be a man.

In many other cases, however, the availability heuristic will lead us astray. This is because events can be memorable for many reasons other than their frequency. Section 5.2, Encoding Meaning, suggested that one good way to encode the meaning of some information is to form a mental image of it. Thus, information that has been pictured mentally will be more available to memory. Indeed, an event that is vivid and easily pictured will trick many people into supposing that type of event is more common than it actually is. Repetition of information will also make it more memorable. So, if the same event is described to you in a magazine, on the evening news, on a podcast that you listen to, and in your Facebook feed; it will be very available to memory. Again, the availability heuristic will cause you to misperceive the frequency of these types of events.

Most interestingly, information that is unusual is more memorable. Suppose we give you the following list of words to remember: box, flower, letter, platypus, oven, boat, newspaper, purse, drum, car. Very likely, the easiest word to remember would be platypus, the unusual one. The same thing occurs with memories of events. An event may be available to memory because it is unusual, yet the availability heuristic leads us to judge that the event is common. Did you catch that? In these cases, the availability heuristic makes us think the exact opposite of the true frequency. We end up thinking something is common because it is unusual (and therefore memorable). Yikes.

The misapplication of the availability heuristic sometimes has unfortunate results. For example, if you went to K-12 school in the US over the past 10 years, it is extremely likely that you have participated in lockdown and active shooter drills. Of course, everyone is trying to prevent the tragedy of another school shooting. And believe us, we are not trying to minimize how terrible the tragedy is. But the truth of the matter is, school shootings are extremely rare. Because the federal government does not keep a database of school shootings, the Washington Post has maintained their own running tally. Between 1999 and January 2020 (the date of the most recent school shooting with a death in the US at of the time this paragraph was written), the Post reported a total of 254 people died in school shootings in the US. Not 254 per year, 254 total. That is an average of 12 per year. Of course, that is 254 people who should not have died (particularly because many were children), but in a country with approximately 60,000,000 students and teachers, this is a very small risk.

But many students and teachers are terrified that they will be victims of school shootings because of the availability heuristic. It is so easy to think of examples (they are very available to memory) that people believe the event is very common. It is not. And there is a downside to this. We happen to believe that there is an enormous gun violence problem in the United States. According the the Centers for Disease Control and Prevention, there were 39,773 firearm deaths in the US in 2017. Fifteen of those deaths were in school shootings, according to the Post. 60% of those deaths were suicides. When people pay attention to the school shooting risk (low), they often fail to notice the much larger risk.

And examples like this are by no means unique. The authors of this book have been teaching psychology since the 1990’s. We have been able to make the exact same arguments about the misapplication of the availability heuristics and keep them current by simply swapping out for the “fear of the day.” In the 1990’s it was children being kidnapped by strangers (it was known as “stranger danger”) despite the facts that kidnappings accounted for only 2% of the violent crimes committed against children, and only 24% of kidnappings are committed by strangers (US Department of Justice, 2007). This fear overlapped with the fear of terrorism that gripped the country after the 2001 terrorist attacks on the World Trade Center and US Pentagon and still plagues the population of the US somewhat in 2020. After a well-publicized, sensational act of violence, people are extremely likely to increase their estimates of the chances that they, too, will be victims of terror. Think about the reality, however. In October of 2001, a terrorist mailed anthrax spores to members of the US government and a number of media companies. A total of five people died as a result of this attack. The nation was nearly paralyzed by the fear of dying from the attack; in reality the probability of an individual person dying was 0.00000002.

The availability heuristic can lead you to make incorrect judgments in a school setting as well. For example, suppose you are trying to decide if you should take a class from a particular math professor. You might try to make a judgment of how good a teacher she is by recalling instances of friends and acquaintances making comments about her teaching skill. You may have some examples that suggest that she is a poor teacher very available to memory, so on the basis of the availability heuristic you judge her a poor teacher and decide to take the class from someone else. What if, however, the instances you recalled were all from the same person, and this person happens to be a very colorful storyteller? The subsequent ease of remembering the instances might not indicate that the professor is a poor teacher after all.

Although the availability heuristic is obviously important, it is not the only judgment heuristic we use. Amos Tversky and Daniel Kahneman examined the role of heuristics in inductive reasoning in a long series of studies. Kahneman received a Nobel Prize in Economics for this research in 2002, and Tversky would have certainly received one as well if he had not died of melanoma at age 59 in 1996 (Nobel Prizes are not awarded posthumously). Kahneman and Tversky demonstrated repeatedly that people do not reason in ways that are consistent with the laws of probability. They identified several heuristic strategies that people use instead to make judgments about likelihood. The importance of this work for economics (and the reason that Kahneman was awarded the Nobel Prize) is that earlier economic theories had assumed that people do make judgments rationally, that is, in agreement with the laws of probability.

Another common heuristic that people use for making judgments is the  representativeness heuristic (Kahneman & Tversky 1973). Suppose we describe a person to you. He is quiet and shy, has an unassuming personality, and likes to work with numbers. Is this person more likely to be an accountant or an attorney? If you said accountant, you were probably using the representativeness heuristic. Our imaginary person is judged likely to be an accountant because he resembles, or is representative of the concept of, an accountant. When research participants are asked to make judgments such as these, the only thing that seems to matter is the representativeness of the description. For example, if told that the person described is in a room that contains 70 attorneys and 30 accountants, participants will still assume that he is an accountant.

inductive reasoning :  a type of reasoning in which we make judgments about likelihood from sets of evidence

inductively strong argument :  an inductive argument in which the beginning statements lead to a conclusion that is probably true

heuristic :  a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

availability heuristic :  judging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

representativeness heuristic:   judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

Type 1 thinking : fast, automatic, and emotional thinking.

Type 2 thinking : slow, effortful, and logical thinking.

• What percentage of workplace homicides are co-worker violence?

Many people get these questions wrong. The answers are 10%; stairs; skin; 6%. How close were your answers? Explain how the availability heuristic might have led you to make the incorrect judgments.

• Can you think of some other judgments that you have made (or beliefs that you have) that might have been influenced by the availability heuristic?

7.3 Problem Solving

• Please take a few minutes to list a number of problems that you are facing right now.
• Now write about a problem that you recently solved.
• What is your definition of a problem?

Mary has a problem. Her daughter, ordinarily quite eager to please, appears to delight in being the last person to do anything. Whether getting ready for school, going to piano lessons or karate class, or even going out with her friends, she seems unwilling or unable to get ready on time. Other people have different kinds of problems. For example, many students work at jobs, have numerous family commitments, and are facing a course schedule full of difficult exams, assignments, papers, and speeches. How can they find enough time to devote to their studies and still fulfill their other obligations? Speaking of students and their problems: Show that a ball thrown vertically upward with initial velocity v0 takes twice as much time to return as to reach the highest point (from Spiegel, 1981).

These are three very different situations, but we have called them all problems. What makes them all the same, despite the differences? A psychologist might define a  problem   as a situation with an initial state, a goal state, and a set of possible intermediate states. Somewhat more meaningfully, we might consider a problem a situation in which you are in here one state (e.g., daughter is always late), you want to be there in another state (e.g., daughter is not always late), and with no obvious way to get from here to there. Defined this way, each of the three situations we outlined can now be seen as an example of the same general concept, a problem. At this point, you might begin to wonder what is not a problem, given such a general definition. It seems that nearly every non-routine task we engage in could qualify as a problem. As long as you realize that problems are not necessarily bad (it can be quite fun and satisfying to rise to the challenge and solve a problem), this may be a useful way to think about it.

Can we identify a set of problem-solving skills that would apply to these very different kinds of situations? That task, in a nutshell, is a major goal of this section. Let us try to begin to make sense of the wide variety of ways that problems can be solved with an important observation: the process of solving problems can be divided into two key parts. First, people have to notice, comprehend, and represent the problem properly in their minds (called  problem representation ). Second, they have to apply some kind of solution strategy to the problem. Psychologists have studied both of these key parts of the process in detail.

When you first think about the problem-solving process, you might guess that most of our difficulties would occur because we are failing in the second step, the application of strategies. Although this can be a significant difficulty much of the time, the more important source of difficulty is probably problem representation. In short, we often fail to solve a problem because we are looking at it, or thinking about it, the wrong way.

problem :  a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

problem representation :  noticing, comprehending and forming a mental conception of a problem

Defining and Mentally Representing Problems in Order to Solve Them

So, the main obstacle to solving a problem is that we do not clearly understand exactly what the problem is. Recall the problem with Mary’s daughter always being late. One way to represent, or to think about, this problem is that she is being defiant. She refuses to get ready in time. This type of representation or definition suggests a particular type of solution. Another way to think about the problem, however, is to consider the possibility that she is simply being sidetracked by interesting diversions. This different conception of what the problem is (i.e., different representation) suggests a very different solution strategy. For example, if Mary defines the problem as defiance, she may be tempted to solve the problem using some kind of coercive tactics, that is, to assert her authority as her mother and force her to listen. On the other hand, if Mary defines the problem as distraction, she may try to solve it by simply removing the distracting objects.

As you might guess, when a problem is represented one way, the solution may seem very difficult, or even impossible. Seen another way, the solution might be very easy. For example, consider the following problem (from Nasar, 1998):

Two bicyclists start 20 miles apart and head toward each other, each going at a steady rate of 10 miles per hour. At the same time, a fly that travels at a steady 15 miles per hour starts from the front wheel of the southbound bicycle and flies to the front wheel of the northbound one, then turns around and flies to the front wheel of the southbound one again, and continues in this manner until he is crushed between the two front wheels. Question: what total distance did the fly cover?

Please take a few minutes to try to solve this problem.

Most people represent this problem as a question about a fly because, well, that is how the question is asked. The solution, using this representation, is to figure out how far the fly travels on the first leg of its journey, then add this total to how far it travels on the second leg of its journey (when it turns around and returns to the first bicycle), then continue to add the smaller distance from each leg of the journey until you converge on the correct answer. You would have to be quite skilled at math to solve this problem, and you would probably need some time and pencil and paper to do it.

If you consider a different representation, however, you can solve this problem in your head. Instead of thinking about it as a question about a fly, think about it as a question about the bicycles. They are 20 miles apart, and each is traveling 10 miles per hour. How long will it take for the bicycles to reach each other? Right, one hour. The fly is traveling 15 miles per hour; therefore, it will travel a total of 15 miles back and forth in the hour before the bicycles meet. Represented one way (as a problem about a fly), the problem is quite difficult. Represented another way (as a problem about two bicycles), it is easy. Changing your representation of a problem is sometimes the best—sometimes the only—way to solve it.

Unfortunately, however, changing a problem’s representation is not the easiest thing in the world to do. Often, problem solvers get stuck looking at a problem one way. This is called  fixation . Most people who represent the preceding problem as a problem about a fly probably do not pause to reconsider, and consequently change, their representation. A parent who thinks her daughter is being defiant is unlikely to consider the possibility that her behavior is far less purposeful.

Problem-solving fixation was examined by a group of German psychologists called Gestalt psychologists during the 1930’s and 1940’s. Karl Dunker, for example, discovered an important type of failure to take a different perspective called  functional fixedness . Imagine being a participant in one of his experiments. You are asked to figure out how to mount two candles on a door and are given an assortment of odds and ends, including a small empty cardboard box and some thumbtacks. Perhaps you have already figured out a solution: tack the box to the door so it forms a platform, then put the candles on top of the box. Most people are able to arrive at this solution. Imagine a slight variation of the procedure, however. What if, instead of being empty, the box had matches in it? Most people given this version of the problem do not arrive at the solution given above. Why? Because it seems to people that when the box contains matches, it already has a function; it is a matchbox. People are unlikely to consider a new function for an object that already has a function. This is functional fixedness.

Mental set is a type of fixation in which the problem solver gets stuck using the same solution strategy that has been successful in the past, even though the solution may no longer be useful. It is commonly seen when students do math problems for homework. Often, several problems in a row require the reapplication of the same solution strategy. Then, without warning, the next problem in the set requires a new strategy. Many students attempt to apply the formerly successful strategy on the new problem and therefore cannot come up with a correct answer.

The thing to remember is that you cannot solve a problem unless you correctly identify what it is to begin with (initial state) and what you want the end result to be (goal state). That may mean looking at the problem from a different angle and representing it in a new way. The correct representation does not guarantee a successful solution, but it certainly puts you on the right track.

A bit more optimistically, the Gestalt psychologists discovered what may be considered the opposite of fixation, namely  insight . Sometimes the solution to a problem just seems to pop into your head. Wolfgang Kohler examined insight by posing many different problems to chimpanzees, principally problems pertaining to their acquisition of out-of-reach food. In one version, a banana was placed outside of a chimpanzee’s cage and a short stick inside the cage. The stick was too short to retrieve the banana, but was long enough to retrieve a longer stick also located outside of the cage. This second stick was long enough to retrieve the banana. After trying, and failing, to reach the banana with the shorter stick, the chimpanzee would try a couple of random-seeming attempts, react with some apparent frustration or anger, then suddenly rush to the longer stick, the correct solution fully realized at this point. This sudden appearance of the solution, observed many times with many different problems, was termed insight by Kohler.

Lest you think it pertains to chimpanzees only, Karl Dunker demonstrated that children also solve problems through insight in the 1930s. More importantly, you have probably experienced insight yourself. Think back to a time when you were trying to solve a difficult problem. After struggling for a while, you gave up. Hours later, the solution just popped into your head, perhaps when you were taking a walk, eating dinner, or lying in bed.

fixation :  when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

functional fixedness :  a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

mental set :  a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

insight :  a sudden realization of a solution to a problem

Solving Problems by Trial and Error

Correctly identifying the problem and your goal for a solution is a good start, but recall the psychologist’s definition of a problem: it includes a set of possible intermediate states. Viewed this way, a problem can be solved satisfactorily only if one can find a path through some of these intermediate states to the goal. Imagine a fairly routine problem, finding a new route to school when your ordinary route is blocked (by road construction, for example). At each intersection, you may turn left, turn right, or go straight. A satisfactory solution to the problem (of getting to school) is a sequence of selections at each intersection that allows you to wind up at school.

If you had all the time in the world to get to school, you might try choosing intermediate states randomly. At one corner you turn left, the next you go straight, then you go left again, then right, then right, then straight. Unfortunately, trial and error will not necessarily get you where you want to go, and even if it does, it is not the fastest way to get there. For example, when a friend of ours was in college, he got lost on the way to a concert and attempted to find the venue by choosing streets to turn onto randomly (this was long before the use of GPS). Amazingly enough, the strategy worked, although he did end up missing two out of the three bands who played that night.

Trial and error is not all bad, however. B.F. Skinner, a prominent behaviorist psychologist, suggested that people often behave randomly in order to see what effect the behavior has on the environment and what subsequent effect this environmental change has on them. This seems particularly true for the very young person. Picture a child filling a household’s fish tank with toilet paper, for example. To a child trying to develop a repertoire of creative problem-solving strategies, an odd and random behavior might be just the ticket. Eventually, the exasperated parent hopes, the child will discover that many of these random behaviors do not successfully solve problems; in fact, in many cases they create problems. Thus, one would expect a decrease in this random behavior as a child matures. You should realize, however, that the opposite extreme is equally counterproductive. If the children become too rigid, never trying something unexpected and new, their problem solving skills can become too limited.

Effective problem solving seems to call for a happy medium that strikes a balance between using well-founded old strategies and trying new ground and territory. The individual who recognizes a situation in which an old problem-solving strategy would work best, and who can also recognize a situation in which a new untested strategy is necessary is halfway to success.

Solving Problems with Algorithms and Heuristics

For many problems there is a possible strategy available that will guarantee a correct solution. For example, think about math problems. Math lessons often consist of step-by-step procedures that can be used to solve the problems. If you apply the strategy without error, you are guaranteed to arrive at the correct solution to the problem. This approach is called using an  algorithm , a term that denotes the step-by-step procedure that guarantees a correct solution. Because algorithms are sometimes available and come with a guarantee, you might think that most people use them frequently. Unfortunately, however, they do not. As the experience of many students who have struggled through math classes can attest, algorithms can be extremely difficult to use, even when the problem solver knows which algorithm is supposed to work in solving the problem. In problems outside of math class, we often do not even know if an algorithm is available. It is probably fair to say, then, that algorithms are rarely used when people try to solve problems.

Because algorithms are so difficult to use, people often pass up the opportunity to guarantee a correct solution in favor of a strategy that is much easier to use and yields a reasonable chance of coming up with a correct solution. These strategies are called  problem solving heuristics . Similar to what you saw in section 6.2 with reasoning heuristics, a problem solving heuristic is a shortcut strategy that people use when trying to solve problems. It usually works pretty well, but does not guarantee a correct solution to the problem. For example, one problem solving heuristic might be “always move toward the goal” (so when trying to get to school when your regular route is blocked, you would always turn in the direction you think the school is). A heuristic that people might use when doing math homework is “use the same solution strategy that you just used for the previous problem.”

By the way, we hope these last two paragraphs feel familiar to you. They seem to parallel a distinction that you recently learned. Indeed, algorithms and problem-solving heuristics are another example of the distinction between Type 1 thinking and Type 2 thinking.

Although it is probably not worth describing a large number of specific heuristics, two observations about heuristics are worth mentioning. First, heuristics can be very general or they can be very specific, pertaining to a particular type of problem only. For example, “always move toward the goal” is a general strategy that you can apply to countless problem situations. On the other hand, “when you are lost without a functioning gps, pick the most expensive car you can see and follow it” is specific to the problem of being lost. Second, all heuristics are not equally useful. One heuristic that many students know is “when in doubt, choose c for a question on a multiple-choice exam.” This is a dreadful strategy because many instructors intentionally randomize the order of answer choices. Another test-taking heuristic, somewhat more useful, is “look for the answer to one question somewhere else on the exam.”

You really should pay attention to the application of heuristics to test taking. Imagine that while reviewing your answers for a multiple-choice exam before turning it in, you come across a question for which you originally thought the answer was c. Upon reflection, you now think that the answer might be b. Should you change the answer to b, or should you stick with your first impression? Most people will apply the heuristic strategy to “stick with your first impression.” What they do not realize, of course, is that this is a very poor strategy (Lilienfeld et al, 2009). Most of the errors on exams come on questions that were answered wrong originally and were not changed (so they remain wrong). There are many fewer errors where we change a correct answer to an incorrect answer. And, of course, sometimes we change an incorrect answer to a correct answer. In fact, research has shown that it is more common to change a wrong answer to a right answer than vice versa (Bruno, 2001).

The belief in this poor test-taking strategy (stick with your first impression) is based on the  confirmation bias   (Nickerson, 1998; Wason, 1960). You first saw the confirmation bias in Module 1, but because it is so important, we will repeat the information here. People have a bias, or tendency, to notice information that confirms what they already believe. Somebody at one time told you to stick with your first impression, so when you look at the results of an exam you have taken, you will tend to notice the cases that are consistent with that belief. That is, you will notice the cases in which you originally had an answer correct and changed it to the wrong answer. You tend not to notice the other two important (and more common) cases, changing an answer from wrong to right, and leaving a wrong answer unchanged.

Because heuristics by definition do not guarantee a correct solution to a problem, mistakes are bound to occur when we employ them. A poor choice of a specific heuristic will lead to an even higher likelihood of making an error.

algorithm :  a step-by-step procedure that guarantees a correct solution to a problem

problem solving heuristic :  a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

confirmation bias :  people’s tendency to notice information that confirms what they already believe

An Effective Problem-Solving Sequence

You may be left with a big question: If algorithms are hard to use and heuristics often don’t work, how am I supposed to solve problems? Robert Sternberg (1996), as part of his theory of what makes people successfully intelligent (Module 8) described a problem-solving sequence that has been shown to work rather well:

• Identify the existence of a problem.  In school, problem identification is often easy; problems that you encounter in math classes, for example, are conveniently labeled as problems for you. Outside of school, however, realizing that you have a problem is a key difficulty that you must get past in order to begin solving it. You must be very sensitive to the symptoms that indicate a problem.
• Define the problem.  Suppose you realize that you have been having many headaches recently. Very likely, you would identify this as a problem. If you define the problem as “headaches,” the solution would probably be to take aspirin or ibuprofen or some other anti-inflammatory medication. If the headaches keep returning, however, you have not really solved the problem—likely because you have mistaken a symptom for the problem itself. Instead, you must find the root cause of the headaches. Stress might be the real problem. For you to successfully solve many problems it may be necessary for you to overcome your fixations and represent the problems differently. One specific strategy that you might find useful is to try to define the problem from someone else’s perspective. How would your parents, spouse, significant other, doctor, etc. define the problem? Somewhere in these different perspectives may lurk the key definition that will allow you to find an easier and permanent solution.
• Formulate strategy.  Now it is time to begin planning exactly how the problem will be solved. Is there an algorithm or heuristic available for you to use? Remember, heuristics by their very nature guarantee that occasionally you will not be able to solve the problem. One point to keep in mind is that you should look for long-range solutions, which are more likely to address the root cause of a problem than short-range solutions.
• Represent and organize information.  Similar to the way that the problem itself can be defined, or represented in multiple ways, information within the problem is open to different interpretations. Suppose you are studying for a big exam. You have chapters from a textbook and from a supplemental reader, along with lecture notes that all need to be studied. How should you (represent and) organize these materials? Should you separate them by type of material (text versus reader versus lecture notes), or should you separate them by topic? To solve problems effectively, you must learn to find the most useful representation and organization of information.
• Allocate resources.  This is perhaps the simplest principle of the problem solving sequence, but it is extremely difficult for many people. First, you must decide whether time, money, skills, effort, goodwill, or some other resource would help to solve the problem Then, you must make the hard choice of deciding which resources to use, realizing that you cannot devote maximum resources to every problem. Very often, the solution to problem is simply to change how resources are allocated (for example, spending more time studying in order to improve grades).
• Monitor and evaluate solutions.  Pay attention to the solution strategy while you are applying it. If it is not working, you may be able to select another strategy. Another fact you should realize about problem solving is that it never does end. Solving one problem frequently brings up new ones. Good monitoring and evaluation of your problem solutions can help you to anticipate and get a jump on solving the inevitable new problems that will arise.

Please note that this as  an  effective problem-solving sequence, not  the  effective problem solving sequence. Just as you can become fixated and end up representing the problem incorrectly or trying an inefficient solution, you can become stuck applying the problem-solving sequence in an inflexible way. Clearly there are problem situations that can be solved without using these skills in this order.

Additionally, many real-world problems may require that you go back and redefine a problem several times as the situation changes (Sternberg et al. 2000). For example, consider the problem with Mary’s daughter one last time. At first, Mary did represent the problem as one of defiance. When her early strategy of pleading and threatening punishment was unsuccessful, Mary began to observe her daughter more carefully. She noticed that, indeed, her daughter’s attention would be drawn by an irresistible distraction or book. Fresh with a re-representation of the problem, she began a new solution strategy. She began to remind her daughter every few minutes to stay on task and remind her that if she is ready before it is time to leave, she may return to the book or other distracting object at that time. Fortunately, this strategy was successful, so Mary did not have to go back and redefine the problem again.

Pick one or two of the problems that you listed when you first started studying this section and try to work out the steps of Sternberg’s problem solving sequence for each one.

a mental representation of a category of things in the world

an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

knowledge about one’s own cognitive processes; thinking about your thinking

individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

Thinking like a scientist in your everyday life for the purpose of drawing correct conclusions. It entails skepticism; an ability to identify biases, distortions, omissions, and assumptions; and excellent deductive and inductive reasoning, and problem solving skills.

a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

an inclination, tendency, leaning, or prejudice

a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

a set of statements in which the beginning statements lead to a conclusion

an argument for which true beginning statements guarantee that the conclusion is true

a type of reasoning in which we make judgments about likelihood from sets of evidence

an inductive argument in which the beginning statements lead to a conclusion that is probably true

fast, automatic, and emotional thinking

slow, effortful, and logical thinking

a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

udging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

noticing, comprehending and forming a mental conception of a problem

when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

a sudden realization of a solution to a problem

a step-by-step procedure that guarantees a correct solution to a problem

The tendency to notice and pay attention to information that confirms your prior beliefs and to ignore information that disconfirms them.

a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

Introduction to Psychology Copyright © 2020 by Ken Gray; Elizabeth Arnott-Hill; and Or'Shaundra Benson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Overview of the Problem-Solving Mental Process

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

• Identify the Problem
• Define the Problem
• Form a Strategy
• Organize Information
• Allocate Resources
• Monitor Progress
• Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

• Asking questions about the problem
• Breaking the problem down into smaller pieces
• Looking at the problem from different perspectives
• Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

• Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
• Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

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You can become a better problem solving by:

• Practicing brainstorming and coming up with multiple potential solutions to problems
• Being open-minded and considering all possible options before making a decision
• Breaking down problems into smaller, more manageable pieces
• Asking for help when needed
• Researching different problem-solving techniques and trying out new ones
• Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors.  The Psychology of Problem Solving .  Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

This is how humans have learned to used tools to solve problems

Human intuition and experience has told us a book can keep a table steady. Image:  Unsplash/Cesar Carlevarino Aragon

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Stay up to date:, future of computing.

• Researchers at MIT’s Center for Brains, Minds and Machines have discovered that humans need 3 critical capabilities to help solve physical problems.
• They include prior knowledge from a similar situation, the ability to imagine the effects of their actions, and a way to rapidly update strategy when they fail.
• The team designed a novel task, the Virtual Tools game, that taps into tool-use abilities - and tried it on humans as well as an AI model.

Human beings are naturally creative tool users. When we need to drive in a nail but don’t have a hammer, we easily realize that we can use a heavy, flat object like a rock in its place. When our table is shaky, we quickly find that we can put a stack of paper under the table leg to stabilize it. But while these actions seem so natural to us, they are believed to be a hallmark of great intelligence — only a few other species use objects in novel ways to solve their problems, and none can do so as flexibly as people. What provides us with these powerful capabilities for using objects in this way?

Have you read?

Why we need a global view of human behaviour, how narratives influence human behaviour, what network science teaches us about human behaviour.

In a new paper published in the Proceedings of the National Academy of Sciences describing work conducted at MIT’s Center for Brains, Minds and Machines , researchers Kelsey Allen, Kevin Smith, and Joshua Tenenbaum study the cognitive components that underlie this sort of improvised tool use. They designed a novel task, the Virtual Tools game , that taps into tool-use abilities: People must select one object from a set of “tools” that they can place in a two-dimensional, computerized scene to accomplish a goal, such as getting a ball into a certain container. Solving the puzzles in this game requires reasoning about a number of physical principles, including launching, blocking, or supporting objects.

The team hypothesized that there are three capabilities that people rely on to solve these puzzles: a prior belief that guides people’s actions toward those that will make a difference in the scene, the ability to imagine the effect of their actions, and a mechanism to quickly update their beliefs about what actions are likely to provide a solution. They built a model that instantiated these principles, called the “Sample, Simulate, Update,” or “SSUP,” model, and had it play the same game as people. They found that SSUP solved each puzzle at similar rates and in similar ways as people did. On the other hand, a popular deep learning model that could play Atari games well but did not have the same object and physical structures was unable to generalize its knowledge to puzzles it was not directly trained on.

This research provides a new framework for studying and formalizing the cognition that supports human tool use. The team hopes to extend this framework to not just study tool use, but also how people can create innovative new tools for new problems, and how humans transmit this information to build from simple physical tools to complex objects like computers or airplanes that are now part of our daily lives.

Kelsey Allen, a PhD student in the Computational Cognitive Science Lab at MIT, is excited about how the Virtual Tools game might support other cognitive scientists interested in tool use: “There is just so much more to explore in this domain. We have already started collaborating with researchers across multiple different institutions on projects ranging from studying what it means for games to be fun, to studying how embodiment affects disembodied physical reasoning. I hope that others in the cognitive science community will use the game as a tool to better understand how physical models interact with decision-making and planning.”

Joshua Tenenbaum, professor of computational cognitive science at MIT, sees this work as a step toward understanding not only an important aspect of human cognition and culture, but also how to build more human-like forms of intelligence in machines. “Artificial Intelligence researchers have been very excited about the potential for reinforcement learning (RL) algorithms to learn from trial-and-error experience, as humans do, but the real trial-and-error learning that humans benefit from unfolds over just a handful of trials — not millions or billions of experiences, as in today’s RL systems,” Tenenbaum says. “The Virtual Tools game allows us to study this very rapid and much more natural form of trial-and-error learning in humans, and the fact that the SSUP model is able to capture the fast learning dynamics we see in humans suggests it may also point the way towards new AI approaches to RL that can learn from their successes, their failures, and their near misses as quickly and as flexibly as people do.”

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The SkillsYouNeed Guide to Interpersonal Skills

Making decisions and solving problems are two key areas in life, whether you are at home or at work. Whatever you’re doing, and wherever you are, you are faced with countless decisions and problems, both small and large, every day.

Many decisions and problems are so small that we may not even notice them. Even small decisions, however, can be overwhelming to some people. They may come to a halt as they consider their dilemma and try to decide what to do.

Small and Large Decisions

In your day-to-day life you're likely to encounter numerous 'small decisions', including, for example:

Tea or coffee?

What shall I have in my sandwich? Or should I have a salad instead today?

What shall I wear today?

Larger decisions may occur less frequently but may include:

Should we repaint the kitchen? If so, what colour?

Should we relocate?

Should I propose to my partner? Do I really want to spend the rest of my life with him/her?

These decisions, and others like them, may take considerable time and effort to make.

The relationship between decision-making and problem-solving is complex. Decision-making is perhaps best thought of as a key part of problem-solving: one part of the overall process.

Our approach at Skills You Need is to set out a framework to help guide you through the decision-making process. You won’t always need to use the whole framework, or even use it at all, but you may find it useful if you are a bit ‘stuck’ and need something to help you make a difficult decision.

Decision Making

Effective Decision-Making

This page provides information about ways of making a decision, including basing it on logic or emotion (‘gut feeling’). It also explains what can stop you making an effective decision, including too much or too little information, and not really caring about the outcome.

A Decision-Making Framework

This page sets out one possible framework for decision-making.

The framework described is quite extensive, and may seem quite formal. But it is also a helpful process to run through in a briefer form, for smaller problems, as it will help you to make sure that you really do have all the information that you need.

Problem Solving

Introduction to Problem-Solving

This page provides a general introduction to the idea of problem-solving. It explores the idea of goals (things that you want to achieve) and barriers (things that may prevent you from achieving your goals), and explains the problem-solving process at a broad level.

The first stage in solving any problem is to identify it, and then break it down into its component parts. Even the biggest, most intractable-seeming problems, can become much more manageable if they are broken down into smaller parts. This page provides some advice about techniques you can use to do so.

Sometimes, the possible options to address your problem are obvious. At other times, you may need to involve others, or think more laterally to find alternatives. This page explains some principles, and some tools and techniques to help you do so.

Having generated solutions, you need to decide which one to take, which is where decision-making meets problem-solving. But once decided, there is another step: to deliver on your decision, and then see if your chosen solution works. This page helps you through this process.

‘Social’ problems are those that we encounter in everyday life, including money trouble, problems with other people, health problems and crime. These problems, like any others, are best solved using a framework to identify the problem, work out the options for addressing it, and then deciding which option to use.

This page provides more information about the key skills needed for practical problem-solving in real life.

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Problem-Solving Mastery: Your Roadmap to Effective Solutions

• Job Skills , Life skills , Soft skills

In today’s rapidly evolving world, problem-solving skills have become more critical. The ability to identify, analyze, and find effective solutions to complex challenges is highly valued across various domains, including education, business, and personal life. Problem-solving skills empower individuals to overcome obstacles, make informed decisions, and confidently navigate uncertain situations. They are key personal and professional success drivers, enabling individuals to adapt to change, innovate, and seize opportunities.

This article will delve into the essential steps for mastering problem-solving skills. We will explore the characteristics of effective problem solvers and highlight the step-by-step process they follow to tackle problems. From defining the problem and gathering information to evaluating solutions and implementing the chosen course of action, we will cover each stage in detail, providing valuable insights and practical strategies. Additionally, we will discuss various techniques and tools that can enhance problem-solving abilities and address common challenges individuals encounter. Whether you are a student, professional, or simply looking to enhance your problem-solving skills, this article will serve as a comprehensive guide to equip you with the necessary knowledge and techniques to become a proficient problem solver.

Understanding Problem Solving

A. definition of problem-solving.

Problem-solving is a fundamental skill applicable across diverse academic, professional, and personal contexts. It plays a crucial role in business, science, engineering, and everyday life, enabling individuals to overcome obstacles, achieve goals, and improve outcomes.

Here are some definitions with sources-

“Problem-solving is the cognitive process of identifying, analyzing, and resolving obstacles or difficulties encountered in order to achieve a desired goal or outcome.”

� Source: – Simon, H. A. (1972). Theories of Bounded Rationality. Decision and Organization, 1(1), 161-176.

� “Problem-solving refers to the systematic approach of finding solutions to challenges by utilizing logical thinking, analytical skills, and creativity.”

Source: – D’Zurilla, T. J., & Nezu, A. M. (2007). Problem-Solving Therapy: A Positive Approach to Clinical Intervention. Springer Publishing Company.

� “Problem-solving is the cognitive process of identifying, analyzing, and overcoming obstacles through the application of problem-solving strategies, critical thinking , and decision-making skills.”

Source: – Fogler, H. S., LeBlanc, S. E., & Rizzo, E. (2020). Strategies for Creative Problem Solving. Pearson.

“Problem-solving involves the ability to define problems, generate potential solutions, evaluate alternatives, and implement the best course of action, resulting in effective decision making and successful resolution of challenges.”

Source: –  Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How People Learn: Brain, Mind, Experience, and School : Expanded Edition. National Academies Press.

B. The role of problem-solving in personal and professional life

The Role of Problem-Solving in Personal and Professional Life:

1. Personal Life:

   a. Decision Making: Problem-solving is crucial in making informed decisions about personal matters, such as career choices, relationships, and financial planning.

   b. Resolving Conflicts: Effective problem-solving skills help resolve conflicts and disputes, fostering healthier relationships and communication.

   c. Adaptability: Problem-solving enables individuals to navigate life’s challenges and adapt to changing circumstances, enhancing personal growth and resilience.

   d. Goal Achievement: By identifying obstacles and finding solutions, problem-solving helps individuals overcome barriers and progress towards achieving personal goals.

2. Professional Life:

   a. Innovation and Creativity: Problem-solving is at the core of innovation, enabling individuals to identify opportunities, develop new ideas, and implement creative solutions.

   b. Decision Making: Effective problem-solving skills aid in making sound business decisions, analyzing data, and evaluating options to achieve desired outcomes.

   c. Troubleshooting and Crisis Management : Problem-solving is crucial in addressing workplace issues, identifying root causes, and implementing solutions to operational challenges and crises.

   d. Collaboration and Teamwork: Problem-solving skills facilitate effective collaboration and teamwork, as individuals work together to analyze problems, generate ideas, and implement solutions.

   e. Continuous Improvement: By identifying inefficiencies and finding better solutions, problem-solving drives continuous improvement in processes, products, and services.

   f. Leadership: Strong problem-solving abilities are essential for effective leadership, as leaders navigate complex situations, inspire teams, and drive organizational success.

Overall, problem-solving is vital in personal and professional life, empowering individuals to overcome obstacles, make informed decisions, foster innovation, and achieve desired outcomes. It promotes adaptability, resilience, and growth, enhancing overall success and satisfaction in various aspects of life.

Characteristics of Effective Problem Solvers

Here are some Characteristics of Effective Problem Solvers:

1. Critical Thinking: Effective problem solvers possess strong critical thinking skills. They can analyze situations objectively, evaluate information, identify patterns, and make logical connections to understand the underlying causes of problems.

2. Analytical Skills: Effective problem solvers can break down complex problems into smaller, more manageable components. They can examine each component individually, identify relevant factors, and assess their interrelationships to understand the problem comprehensively.

3. Creativity and Innovative Thinking: Effective problem solvers think outside the box and are open to unconventional ideas and solutions. They approach problems creatively, seeking new perspectives, alternative approaches, and innovative solutions.

4. Persistence and Resilience: Effective problem solvers persevere when facing obstacles and setbacks. They are resilient and maintain a positive attitude, viewing challenges as opportunities for growth and learning rather than insurmountable barriers.

5. Adaptability and Flexibility: Effective problem solvers are adaptable and flexible in their thinking and approach. They are open to adjusting their strategies, considering different viewpoints, and embracing change as they navigate complex problem-solving situations.

6. Systems Thinking: Effective problem solvers consider the larger context and understand the interconnectedness of various factors. They can see how different elements within a system influence each other and recognize the ripple effects of their decisions and actions.

7. Collaboration and Communication: Effective problem solvers are skilled in collaboration and communication. They actively listen to others, seek input and feedback, and can articulate their thoughts and ideas clearly. They can work well in teams, leveraging diverse perspectives and expertise to find optimal solutions.

8. Decision Making: Effective problem solvers are proficient in decision-making . They gather relevant information, weigh different options, assess risks and benefits, and make informed choices based on a logical and rational evaluation process.

9. Continuous Learning: Effective problem solvers have a growth mindset and a thirst for knowledge. They actively seek opportunities to learn new skills, expand their knowledge base, and stay updated on industry trends and advancements.

10. Emotional Intelligence: Effective problem solvers possess emotional intelligence, allowing them to understand and manage their emotions and empathize with others. They can navigate interpersonal dynamics, handle conflicts constructively, and foster positive relationships while solving problems.

These characteristics collectively contribute to the effectiveness of problem solvers, enabling them to approach challenges with a systematic, innovative, and resilient mindset, ultimately leading to successful problem resolution and achieving desired outcomes.

The Problem-Solving Process

Here is The Problem-Solving Process Step by Step:

Step 1: Defining the Problem:

1. Identifying the root cause: To effectively solve a problem, it’s important to identify the underlying cause or causes. This involves digging deeper to understand the factors or circumstances that led to the problem’s occurrence.

2. Clarifying the desired outcome: Clearly defining the desired outcome provides a clear direction for problem-solving. It helps in setting goals and measuring the success of the solution.

Step 2: Gathering information and analyzing the situation:

1. Collecting relevant data and facts: Gathering relevant data and facts about the problem is crucial for making informed decisions. This involves collecting information from reliable sources, conducting surveys, interviews, or analyzing existing data.

2. Conducting research and seeking different perspectives: Researching the problem and seeking different perspectives allows for a comprehensive understanding of the situation. This may involve studying case studies, consulting experts, or getting insights from people who have faced similar challenges.

Step 3: Generating potential solutions:

1. Brainstorming techniques: Brainstorming involves generating many ideas without judgment. It encourages creativity and open-mindedness, allowing for the exploration of various solutions.

2. Considering multiple options: Considering multiple options helps in expanding the range of possibilities. It involves evaluating different approaches, strategies, or alternatives to find the most effective solution.

Step 4: Evaluating and selecting the best solution:

1. Assessing pros and cons: Evaluating the potential solutions involves assessing their advantages and disadvantages. This helps in understanding the potential benefits and drawbacks of each option.

2. Using decision-making tools and techniques: Decision-making tools and techniques, such as decision matrices, cost-benefit analysis, or SWOT analysis, can provide a structured approach to evaluating and comparing different solutions. They help in making an informed decision.

Step 5: Implementing the chosen solution:

1. Developing an action plan: A detailed action plan outlines the steps and tasks needed to implement the chosen solution. It includes setting deadlines, assigning responsibilities, and allocating necessary resources.

2. Overcoming potential obstacles: Anticipating potential obstacles and challenges helps develop contingency plans. By identifying potential barriers in advance, proactive measures can be taken to overcome them and ensure a smoother implementation process.

Step 6: Monitoring and evaluating the outcomes:

1. Assessing the solution’s effectiveness: Regularly monitoring and evaluating the outcomes of the implemented solution is crucial. This involves measuring the results against the desired outcome and assessing whether the solution effectively addresses the problem.

2. Making adjustments if necessary: If the desired outcomes are not achieved or new issues arise, it may be necessary to adjust the solution or implementation plan. This ensures continuous improvement and adaptability throughout the problem-solving process.

By following this step-by-step process, individuals and teams can approach problem-solving systematically and comprehensively, increasing the chances of finding effective solutions and achieving desired outcomes.

Techniques and Strategies for Effective Problem Solving

Here are some Techniques and Strategies for Effective Problem Solving:

A. SWOT analysis (Strengths, Weaknesses, Opportunities, Threats):

SWOT analysis is a widely used technique for understanding a situation or organization’s internal strengths and weaknesses and the external opportunities and threats it faces. It involves identifying and analyzing these four factors to gain insights into the current state and potential future scenarios. One can effectively capitalize on opportunities and mitigate threats by understanding strengths and weaknesses.

B. Root cause analysis:

Root cause analysis is a technique used to identify the underlying cause or causes of a problem. It involves digging deeper into the problem to determine the fundamental reasons for its occurrence. By identifying and addressing the root cause, rather than just treating symptoms, one can prevent the problem from recurring and find long-term solutions.

C. Pareto analysis:

Pareto analysis, also known as the 80/20 rule, is a technique that helps prioritize tasks or issues based on their significance. It involves identifying the vital few (20%) contributing to the majority (80%) of the problem. One can achieve the greatest impact with limited resources by focusing efforts on addressing the most significant factors.

D. Six Thinking Hats technique:

The Six Thinking Hats technique, developed by Edward de Bono, is a method for approaching problem-solving from different perspectives. Each “hat” represents a different thinking mode or mindset, such as logical, creative, critical, etc. By consciously adopting these different perspectives, individuals or teams can explore different angles, consider various factors, and enhance problem-solving.

E. Design thinking approach:

The design thinking approach is a human-centered problem-solving methodology. It emphasizes empathy, collaboration, and experimentation to understand the user’s needs, ideate innovative solutions, and iterate through prototypes. It involves several stages, including empathizing with users, defining the problem, ideating potential solutions, prototyping, and testing. This approach encourages a creative and iterative problem-solving process that delivers solutions meeting user needs.

By utilizing these techniques and strategies for effective problem-solving, individuals and teams can enhance their problem-solving capabilities, think more critically and creatively, and arrive at comprehensive and innovative solutions to address various challenges.

Overcoming Common Challenges in Problem-Solving�

Now we discuss how to overcome Common Challenges in Problem-Solving:

A. Emotional barriers and biases:

1. Self-awareness: Recognize and acknowledge your emotions and biases that may hinder the problem-solving process.

2. Objective perspective: Strive to approach problems with an open mind and consider alternative viewpoints.

3. Seek feedback: Involve others in problem-solving to gain diverse perspectives and challenge your biases.

B. Fear of failure and risk aversion:

1. Embrace a growth mindset: View failures as learning opportunities and be open to taking calculated risks.

2. Break problems into smaller steps: Breaking down complex problems into smaller, manageable tasks can help reduce the Fear of failure.

3. Experiment and iterate: Implement solutions in iterative stages, allowing for adjustments and learning from setbacks.

C. Lack of communication and collaboration:

1. Active listening: Listen attentively to others’ perspectives, fostering effective communication and understanding.

2. Encourage participation: Create a supportive environment where everyone feels comfortable contributing ideas and insights.

3. Foster teamwork: Promote collaboration and establish clear roles and responsibilities within problem-solving teams.

D. Ineffective time management:

1. Prioritize tasks: Identify the most critical aspects of the problem and allocate time accordingly.

2. Set deadlines and milestones: Establish specific deadlines for each step of the problem-solving process to stay on track.

3. Avoid distractions: Minimize interruptions and focus on the task by creating a conducive work environment.

By addressing these common problem-solving challenges, individuals and teams can enhance their problem-solving effectiveness and achieve better outcomes. Overcoming emotional barriers and biases, embracing risk-taking, fostering effective communication and collaboration, and managing time efficiently are key factors in successful problem-solving endeavors. By developing strategies to tackle these challenges, individuals can unlock their problem-solving potential and approach challenges with confidence and resilience.

Developing Problem-Solving Skills�

Is it possible to develop problem-solving skills? Yes, it is possible. But How?�

A. Continuous learning and skill development:

1. Stay curious: Cultivate a continuous learning mindset by seeking new knowledge, exploring different perspectives, and staying updated on industry trends.

2. Acquire relevant knowledge: Develop a solid foundation in the areas relevant to problem-solving, such as critical thinking, analytical skills, creativity, and decision-making.

3. Pursue professional development: Attend workshops, training programs, and online courses on problem-solving techniques and strategies.

B. Seeking feedback and reflection:

1. Welcome constructive criticism: Seek feedback from peers, mentors, or supervisors to gain insights into areas for improvement in your problem-solving approach.

2. Reflect on past experiences: Evaluate your problem-solving efforts, identify strengths and weaknesses, and learn from your successes and failures.

3. Develop self-awareness: Understand your thinking patterns, biases, and emotional reactions to improve your problem-solving skills.

C. Practicing problem-solving exercises and scenarios:

1. Solve puzzles and brain teasers: Engage in activities that challenge your problem-solving abilities, such as puzzles, riddles, or logic games.

2. Simulate problem-solving scenarios: Create hypothetical problem-solving situations and brainstorm potential solutions to enhance your critical thinking and decision-making skills.

3. Participate in group problem-solving activities: Collaborate with others in problem-solving exercises or workshops to foster teamwork and develop effective communication skills.

D. Engaging in real-life problem-solving experiences:

1. Embrace challenges: Seek opportunities to tackle real-world problems, whether at work, in personal projects, or community initiatives.

2. Apply problem-solving techniques: Utilize the problem-solving process and relevant strategies to address issues encountered in various aspects of life.

3. Learn from experiences: Reflect on your problem-solving approach in real-life situations, identify areas of improvement, and adjust your strategies accordingly.

Developing problem-solving skills is an ongoing process that requires continuous learning, practice, and application in both simulated and real-life scenarios. By investing time and effort in skill development, seeking feedback, reflecting on experiences, and engaging in problem-solving activities, individuals can strengthen their problem-solving abilities and effectively address complex challenges.

Applying Problem-Solving Skills in Different Areas

Now we will discuss Applying Problem-Solving Skills in Different Areas:

A. Problem-solving in the workplace:

Problem-solving skills are highly valuable in the workplace as they enable individuals to address challenges, make informed decisions, and contribute to organizational success. In a professional setting, problem-solving involves identifying and analyzing issues, generating effective solutions, and implementing them to achieve desired outcomes. It often requires collaboration, critical thinking, and creative problem-solving techniques. Effective problem-solving in the workplace can lead to increased productivity, improved teamwork, and innovation.

B. Problem-solving in personal relationships:

Problem-solving skills play a crucial role in maintaining healthy and constructive personal relationships. Conflicts and challenges are inevitable with family members, friends, or romantic partners. Applying problem-solving skills in personal relationships involves active listening, empathy, and open communication. It requires identifying and understanding the issues, finding common ground, and working towards mutually beneficial solutions. Problem-solving in personal relationships helps build trust, strengthen connections, and promote harmony.

C. Problem-solving in entrepreneurship:

Problem-solving is an essential skill for entrepreneurs, as it drives innovation and the ability to identify and seize opportunities. Entrepreneurs face various challenges, such as market competition, resource constraints, and changing customer needs. Applying problem-solving skills in entrepreneurship involves identifying market gaps, analyzing customer pain points, and developing innovative solutions. Entrepreneurs must be adaptable, resilient, and creative in finding solutions that address real-world problems and create customer value.

D. Problem-solving in everyday life:

Problem-solving skills are not limited to specific areas but are applicable in everyday life. From simple tasks to complex decisions, problem-solving helps navigate challenges efficiently. Everyday problem-solving involves assessing situations, setting goals, considering available resources, and making informed choices. It can range from troubleshooting technology issues to managing personal finances, resolving conflicts, or finding solutions to logistical problems. Developing problem-solving skills in everyday life leads to increased self-confidence, improved decision-making abilities, and overall personal effectiveness.

In all these areas, applying problem-solving skills enables individuals to approach challenges with a structured and analytical mindset, find practical solutions, and overcome obstacles effectively. It empowers individuals to think critically, adapt to changing circumstances, and positively contribute to various aspects of their lives.

Case Studies of Successful Problem Solving

Here are some Case Studies of Successful Problem Solving:

A. Real-life examples of problem-solving success stories:

1. NASA’s Apollo 13 Mission: The Apollo 13 mission faced a critical problem when an oxygen tank exploded, jeopardizing the lives of the astronauts. Through collaborative problem-solving, the NASA team on the ground and the astronauts in space worked together to develop innovative solutions, such as building a makeshift CO2 filter, conserving power, and navigating a safe return to Earth.

2. Apple’s iPhone Development: Apple faced the challenge of creating a revolutionary smartphone that combined multiple functions in a user-friendly design. Through rigorous problem-solving, Apple’s team developed groundbreaking solutions, such as the touch screen interface, intuitive user experience, and integration of various technologies, leading to the successful launch of the iPhone.

3. Toyota’s Lean Manufacturing System: Toyota encountered production inefficiencies and quality issues. By implementing problem-solving techniques, such as the Toyota Production System, the company focused on waste reduction, continuous improvement, and empowering employees to identify and solve problems. This increased productivity, improved quality, and a competitive advantage in the automotive industry.

B. Analysis of the problem-solving strategies employed:

1. Collaborative Problem-Solving: Successful problem-solving often involves collaboration among individuals or teams. Organizations can tackle complex challenges more effectively by leveraging diverse perspectives, knowledge, and skills.

2. Innovative Thinking: Problem-solving success stories often involve innovative thinking to address issues in novel ways. This may include exploring new technologies, challenging conventional wisdom, or adopting creative approaches that disrupt the status quo.

3. Systematic Approach: Effective problem-solving requires a systematic approach that involves defining the problem, gathering relevant information, analyzing options, and implementing solutions. This structured method provides a comprehensive understanding of the problem and helps identify the most appropriate action.

4. Continuous Improvement: Many successful problem-solving cases are committed to continuous improvement. Organizations embracing a learning and adaptability culture are better equipped to identify and solve problems efficiently, leading to long-term success.

5. Customer-Centric Solutions: Problem-solving strategies that prioritize understanding and meeting customer needs tend to yield successful outcomes. Organizations can develop solutions that deliver value and drive customer satisfaction by placing the customer at the center of problem-solving efforts.

Analyzing the problem-solving strategies employed in these case studies provides valuable insights into the approaches, techniques, and mindsets that contribute to successful problem resolution. It highlights the importance of collaboration, innovation, systematic thinking, continuous improvement, and customer focus in achieving positive outcomes.

Conclusion:

In conclusion, problem-solving skills are vital in various aspects of life, including personal, professional, and entrepreneurial endeavors. Through this article, we have explored the importance of problem-solving, its Definition, its role in different areas, characteristics of effective problem solvers, the problem-solving process, and techniques for effective problem-solving. We have also examined case studies of successful problem-solving and analyzed the strategies employed.

Recap of key points:

1. Problem-solving skills are crucial for personal, professional, and entrepreneurial success.

2. Effective problem solvers possess critical thinking, creativity, adaptability, and perseverance.

3. The problem-solving process involves defining the problem, gathering information, generating solutions, evaluating options, implementing the chosen solution, and monitoring outcomes.

4. Techniques like SWOT analysis, root cause analysis, Pareto analysis, Six Thinking Hats, and design thinking provide valuable frameworks for problem-solving.

As you have learned about the importance and various aspects of problem-solving, I encourage you to apply these skills in your own life. Problem-solving is not a mere intellectual exercise but a practical tool that can lead to personal growth, professional success, and positive societal contributions. Developing and honing your problem-solving abilities allows you to navigate challenges, make informed decisions, and find innovative solutions.

Embrace a continuous improvement mindset and a willingness to think outside the box. Seek opportunities to apply problem-solving skills in your relationships, workplace, entrepreneurial ventures, and everyday life. Remember that each challenge presents an opportunity for growth and learning. You can overcome obstacles and achieve desired outcomes by approaching problems with a structured and analytical mindset, considering multiple perspectives, and employing effective problem-solving techniques.

Incorporate problem-solving into your daily life and encourage others to do the same. By doing so, you contribute to a more proactive and solution-oriented society. Remember, problem-solving is a skill that can be developed and refined through practice and experience. So, take on challenges, embrace creativity, and be a proactive problem solver.

Start applying problem-solving skills today, and you will witness the positive impact it can have on your life and the lives of those around you.

ORIGINAL RESEARCH article

Personality traits and complex problem solving: personality disorders and their effects on complex problem-solving ability.

• 1 College of Education, Institute of Educational Sciences and Research, Salzburg, Austria
• 2 Department of Psychology, University of Greifswald, Greifswald, Germany
• 3 Department of Psychology, University of Graz, Graz, Austria
• 4 Department of Psychiatry, Psychotherapy and Psychosomatics, Paracelsus Medical Private University, Salzburg, Austria
• 5 Institute of Synergetics and Psychotherapy Research, Paracelsus Medical Private University, Salzburg, Austria

Complex problem solving (CPS) can be interpreted as the number of psychological mechanisms that allow us to reach our targets in difficult situations, that can be classified as complex, dynamic, non-transparent, interconnected, and multilayered, and also polytelic. The previous results demonstrated associations between the personality dimensions neuroticism, conscientiousness, and extraversion and problem-solving performance. However, there are no studies dealing with personality disorders in connection with CPS skills. Therefore, the current study examines a clinical sample consisting of people with personality and/or depressive disorders. As we have data for all the potential personality disorders and also data from each patient regarding to potential depression, we meet the whole range from healthy to impaired for each personality disorder and for depression. We make use of a unique operationalization: CPS was surveyed in a simulation game, making use of the microworld approach. This study was designed to investigate the hypothesis that personality traits are related to CPS performance. Results show that schizotypal, histrionic, dependent, and depressive persons are less likely to successfully solve problems, while persons having the additional behavioral characteristics of resilience, action orientation, and motivation for creation are more likely to successfully solve complex problems.

Introduction

A problem arises when a person is unable to reach the desired goal. Problem-solving refers to the cognitive activities aimed at removing the obstacle separating the present situation from the target situation ( Betsch et al., 2011 ). In our daily lives, we are constantly confronted with new challenges and a plethora of possibilities to address them. Accordingly, problem-solving requires the ability to identify these possibilities and select the best option in the unfamiliar situations. It is, therefore, an important competence to deal with new conditions, adapt to changing circumstances, and react flexibly to new challenges ( Kipman, 2020 ).

Even tasks for which the sequence of choices to be taken is relatively straight-forward, such as in the process of navigating to a certain destination in a foreign city or cooperative decision-making during psychotherapy, appear as a highly diversified process, when considered in detail ( Schiepek, 2009 ; Schiepek et al., 2016a ). However, most problems we face in everyday life are not as well defined and do not necessarily have an unambiguous solution. The ability to deal with such sophisticated problems, i.e., complex problem solving (CPS) , is of particular relevance in everyday settings.

Funke (2001 , 2003 , 2012) and Dörner and Funke (2017) , identified five dimensions along which complex problems can be characterized: (i) The complexity of the problem arises from the number of variables contributing to the problem, which in turn affect the number of possible solutions. (ii) The connectivity of the problem arises from the number of interconnections between these variables. (iii) The dynamics of the problem arise from changes in the problem variables or their interconnections over time. These changes can be a result of the person’s actions or are inherent to the problem, i.e., characteristics of the variables themselves or a result of interactions between the variables. (iv) The non-transparency of a problem refers to the extent to which the target situation, the variables involved, their interactions and dynamics cannot be ascertained. (v) Finally, complex problems are usually polytelic , i.e., they have more than one target situation.

Accordingly, CPS requires the ability to model the problem space, i.e., understand which variables are involved and how they are interconnected, the ability to handle a large number of variables at the same time, judge the relevance and success probability of possibilities, identify the interconnections between variables and the inherent dynamics thereof, judge the consequences of one’s own actions with regards to the problem space, and collect relevant knowledge to deal with non-transparency.

Tasks to measure this complex set of abilities were developed by Dörner (1980 , 1986) , who criticized that the measurement of general intelligence tended to use simple tasks that are not comparable with the level of complexity of real-world problems. He proposed measuring intelligent behavior in computerized environments specifically adapted to simulate the properties of sophisticated problems in everyday settings ( Danner et al., 2011b ). cf. Dörner et al. (1983) in research used settings referred to as Microworlds to assess the way participants acted under heterogeneous, dynamic, and non-transparent conditions. Participants were instructed to administrate a tiny German village by the name of Lohhausen by creating the ideal conditions for the village and its inhabitants ( Hussy, 1998 , p. 140–141). This microworld comprised more than 2,000 variables, guaranteeing an elevated level of complexity, which also required a high-level operationalization of CPS. However, the general validity of the performance at Lohhausen turned out to be a questionable issue, since the performance was operationalized as a factor composed of 6 main criteria, some of which were subjective assessments. Accordingly, the parameter definition for CPS performance was rather ambiguous. The reason for this ambiguity is that the vague description of the objective, i.e., to establish a respectable standard of well-being among the inhabitants—gave room for subjective interpretation (cf. Hussy, 1998 , p. 146–150). Since then, the psychometric validity of the CPS performance in complex microworlds has been demonstrated by several researchers (e.g., Wittmann and Hattrup, 2004 ; Danner et al., 2011a ).

Because of the high-translational relevance of the topic, the question arises how and which individual differences contribute to more or less efficient solving of the complex problems, such as Microworlds. Individual differences in problem-solving have been described along a cognitive dimension, i.e., the problem-solving style , and an emotional–motivational dimension, i.e., the problem orientation ( D’Zurilla et al., 2011 ). Cognitively, problems can be solved in a rational style , i.e., systematically and deliberate, in an impulsive style , i.e., careless, hurried, and often incomplete, or in an avoidance style via passivity and inaction leading to procrastination ( D’Zurilla et al., 2002 , as cited in D’Zurilla et al., 2011 ). Emotionally, people with a positive problem orientation , see problems as an opportunity for success, i.e., a “challenge” and are confident that the problem is solvable, and that they will be able to solve it. People with a negative problem orientation view problems as an opportunity for failure, i.e., a “threat” and doubt their ability to solve the problem ( D’Zurilla et al., 2011 ).

Some studies have already related basic personality traits, such as the BIG-5, to the way a person tackles complex problems. For example, it has been demonstrated that individuals who score high in conscientiousness, openness for experience, and extraversion also have higher problem-solving abilities. In contrast, individuals with higher scores in neuroticism show poor problem-solving abilities ( D’Zurilla et al., 2011 ). McMurran et al. (2001) demonstrate that this is a result of the way in which neurotic individuals approach problems. Neuroticisms was significantly associated with an impulsive or avoidant problem-solving style, and a negative problem orientation. Vice versa, Arslan (2016) identified a positive relationship between constructive problem-solving and being extrovert, receptive, and open to new learning experiences, and also high in tolerability and accountability.

The present study seeks to extend these findings to individuals with “extreme” levels of personality traits, i.e., individuals with personality disorders, taking into consideration the way in which personality characteristics manifest in everyday situations, such as work–place situations. Following the most current diagnostic approach to personality disorders as outlined in the ICD-11, the individual accentuations of 9 disorder-relevant personality traits were taken into account, including:

(i) Paranoid traits , i.e., the extent of mistrust toward others.

(ii) Schizoid traits , i.e., the inability to express feelings and experience pleasure, resulting in fierce separation from affective contacts and also friends and social gatherings with an excessive preference for the magical worlds.

(iii) Antisocial traits , i.e., the extent of disregard for social obligations and callous lack of involvement in feelings for others, resulting in aggressive behavior.

(iv) Borderline traits , i.e., the tendency to act out impulses without regard to consequences, associated with unpredictable and erratic moods.

(v) Histrionic traits , i.e., the tendency to overdramatize and show a theatrical, exaggerated expression of feelings, suggestibility, egocentricity, hedonism, and a constant desire for recognition, external stimuli, and attention.

(vi) Dependent traits , i.e., excessive and inappropriate agreeableness ( Costa and McCrae, 1986 ) resulting in major anxiety about separation, feelings of helplessness, and a tendency to subordinate oneself to the desires of others.

(vii) Schizotypal traits , i.e., extreme levels of introversion, resulting in social disengagement.

(viii) Obsessive-compulsive (anankastic) traits , i.e., excessive conscientiousness, involving feelings of doubt, perfectionism, and inflexibility.

(ix) Depressive traits , i.e., the tendency toward persistent feelings of sadness and loss of interest.

Few studies have assessed problem-solving, much less CPS, in patients with personality disorders. Previous research shows, that patients with histrionic and narcissistic personality types show an impulsive problem-solving style, whereas avoidant and dependent individuals show a negative problem orientation ( McMurran et al., 2007 ). In addition, people who are in a depressive mood ( Lyubomirsky et al., 1999 ), or even clinically depressed and anxious have difficulties generating effective solutions to problems ( Marx et al., 1992 ). Accordingly, we hypothesize a negative association between high accentuations of disorder-related personality traits and CPS. The aim of the present study was to explore, which disorders were most severely affected and whether this association also manifested in work-related situations.

Action-orientated problem-solving is particularly required in areas where people are under a lot of stress, for example, in entrepreneurship, team leading in the clinical settings, or firefighting. Especially when a work-related crisis appears, action-oriented problem-solving is important, because it unites handling both novel and routine demands ( Rudolph and Repenning, 2002 , as cited in Rudolph et al., 2009 ). Rudolph et al. (2009) found that only by taking action, information cues become available. Accordingly, both CPS and everyday situations in the work-place require the ability to cope with stressful events and protect oneself from the negative effects of stress, i.e., resilience ( Lee and Cranford, 2008 , as cited in Wagnild and Young, 1993 ; Fletcher and Sarkar, 2013 ). Indeed, individuals with a high trait resilience are more willing to take action in problem-solving ( Li and Yang, 2009 , as cited in Li et al., 2013 ). This is consistent with previous research demonstrating that effective problem-solving abilities go along with high-psychological resilience ( Garcia-Dia et al., 2013 ; Williamson et al., 2013 ; Crowther et al., 2016 , as cited in Pinar et al., 2018 ). Pinar et al. (2018) even found that problem-solving competencies can be increased by increasing psychological resilience and self-confidence levels. Accordingly, identifying which personality disorders are most severely affected in these areas may also provide hints for psychotherapy.

Materials and methods

Participants.

The present study included data from N = 242 adults (49.1% male) with personality disorders and/or depressive disorders, with ages ranging from 17 to 48 years (mean: 26.5 years). The participants were given five assessment batteries and a set of demographic variables, which included game experience. They were also given a commercial complex problem-solving (CPS) game known as Cities: Skylines involving the construction and managing of a city like a mayor would with the goal of growing the city while not running out of money. Participants were patients from psychiatric and psychosomatic hospitals, who got follow-up treatment directly after leaving the hospital. The treatment took place in a panel practice for aftercare where the CPS experiment was done (see Figure 1 ).

Figure 1. Exemplary model of some (not all) factors that influence the number of inhabitants and the general happiness of the population in Cities: Skylines (CSL). The number of related variables illustrates the complexity, connectivity, and polytely in the simulated environment.

Personality questionnaires

In order to obtain a comprehensive diagnosis and measure disordered personality traits in a continuous fashion, three personality questionnaires were used, including the PSSI, SCID-5-PD, and MMPI-II. While the PSSI scores were used in the statistical analysis, SCID-5-PD scores and MMPI-II scores were used to confirm the PSSI diagnosis. Furthermore, in order to assess the manifestation of disordered personality traits in work-related situations, we used the BIP.

The Persönlichkeits-Stil und Störungs-Inventar (PSSI; Kuhl and Kazen, 2009 ) is a self-report instrument that measures the comparative manifestation of the character traits. These are designed as non-pathological analogs of the personality disorders described in the psychiatric diagnostic manuals DSM-IV and ICD-10. The PSSI comprises 140 items assigned to 14 scales: PN (willful-paranoid), SZ (independent-schizoid), ST (intuitive-schizotypal), BL (impulsive-borderline), HI (agreeable-histrionic), NA (ambitious-narcissistic), SU (self-critical-avoidant), AB (loyal-dependent), ZW (conscientious-compulsive—anankastic), NT (critical-negativistic), DP (calm-depressive), SL (helpful-selfless), RH (optimistic-rhapsodic), and AS (self-assertive-antisocial). Patients rate each item on a 4-point Likert scale (from 0 to 3) and continuous scale values are calculated as the sum of the 10 item ratings belonging to a scale. Accordingly, a maximum value of 30 can be achieved for each scale. In this study, we focused on the nine traits PN, SZ, ST, BL, HI, AB, ZW, DP, and AS, as the other measured traits are not listed as personality disorders in the ICD-10 or DSM-V.

The Strukturiertes Klinisches Interview für DSM-5—Persönlichkeitsstörungen (SCID-5-PD; First et al., 2019 ) is a semi-structured diagnostic questionnaire that can be used to evaluate the 10 personality disorders included in the DSM-5 in clusters A, B, and C, as well as disorders in the category “not otherwise specified personality disorder.” Each DSM-5 criterion is assigned corresponding interview questions to assist the interviewer in assessing the criterion. It is possible to utilize the SCID-5-PD to categorically diagnose personality disorders (present or absent) ( First et al., 2019 ). In addition, regulations are also included which can be used to create dimensional ratings.

The MMPI ® –2 ( Butcher et al., 2000 ) is a revised and completely re-normed version of the Minnesota Multiphasic Personality Inventory (MMPI). With the help of the MMPI ® –2, a relatively complete picture of the personality structure can be obtained in an economical way.

The Bochumer Inventar zur berufsbezogenen Persönlichkeitsbeschreibung (BIP; Hossiep and Paschen, 2019 ) measures personality traits in a work-related context. A total of 210 items are assigned to 4 global dimensions including 14 subscales. These include work orientation (diligence, agility, and focus), professional approach ( performance-, creativity-, and management motivation), social competencies (sensitivity, social skills, sociability, teamwork, and assertiveness), and mental constitution (emotional stability, resilience, and self-confidence) on a continuous scale. Patients respond to each item on a 6-point Likert scale.

Game experience

As possible previous experience with the CPS game may affect the level of problem-solving efficiency during the test, participants were asked to rate their previous engagement with simulation-based urban development games on a 4-point Likert scale with response options running from “none” to “very much.” The same poll also featured a listing of 20 symbols from Cities: Skylines, in combination with their meanings (e.g., “no electricity”) for participants to make use of during their quest. At the end, participants were asked to rate their experience based on a 5-point scaling reaching from 1 (extremely simple) to 5 (super challenging). At last, the researcher also marked on each poll sheet, whether (a) the individual patient was able to accomplish the mission (Success, Failure, or Patient Breakup), and (b) the exact time frame of the testing session (morning, afternoon, or evening).

Cities: Skylines (CSL)

The computer-based simulation game Cities: Skylines ( Paradox Interactive, 2015a ), which can be downloaded from Steam for about 30 dollars, explores the construction and management of a city and was implemented in the current study as a Microworld scenario. Much like in the successful microworld Lohhausen ( Dörner et al., 1983 ), gamers in Cities: Skylines basically act in lieu of the city’s mayor, taking over all of his authority and duties. As promised in the user manual, it “offers endless sandbox play in a city that keeps offering new areas, resources, and technologies to explore, continually presenting the player with new challenges to overcome” ( Paradox Interactive, 2015b , p. 4). The game fulfills the parameters of Brehmer and Dörner’s (1993) microworlds and meets the standards of complex problems according to Funke ’s ( 2001; 2012 ). The examples below illustrate the way in which these features are relevant for Cities: Skylines (see Figure 2 ; see also de Kooter, 2015 ; Paradox Interactive, 2015b ):

Figure 2. Procedure of the study.

(i) Complexity is fulfilled because the system is made up of a variety of components including a vast series of different constructions (areas, basic resources, roads, constructions, electricity, water supplies, etc.), options (fiscal matters, budgeting, credit, traffic management, security, healthcare, and education), and parameters (population density, inhabitant satisfaction, environmental issues, and delinquency). As an example, while purchasing a wind turbine, the participant may weigh related costs, budgeted funds for the week, potential noise pollution, the way the turbine blends into the landscape vs. the rate of efficiency, along with the hardware required to connect the device to the town’s existing network, etc.

(ii) Connectivity is fulfilled because the parameters in the model are heavily interconnected. Each component is related to at least one other element (see Figure 2 ) implementing a network of correlations and interdependencies. As an example, residential zones should not be located in proximity to wind turbines, as the amount of noise pollution caused by their operation might affect the quality of life in that zone, which again might make the area less attractive and lower the property values.

(iii) Dynamics are fulfilled because the demands of the population are subject to autonomous change, while other variables, e.g., zoning requirements also depend in part on the actions of the participants. While the dynamics of the game cause the population and the territory of the city to grow, the whole infrastructure becomes inadequate over time and needs to be adapted. Water and electricity infrastructures, the number of schools, clinics, municipal cemeteries, etc., that used to suffice for the population then need to be expanded. Moreover, depending on its frequentation, each building or road has a certain life span until it is left abandoned and will have to be replaced.

(iv) Non-transparency is not featured as an essential part of the Cities: Skylines gameplay, but is instead primarily caused by its connectivity and intricacy. While playing the game, the number of variables and their interconnections make active exploration essential. Independent of the player’s actions; however, there are also very non-transparent features, such as random death waves or an (unexpectedly) higher incidence of fires in the area following the first construction of a firefighter center by the player.

(v) Polytely arises since the objective to increase the population of the city requires the simultaneous achievement of a large number of minor tasks, which may be conflicting (e.g., strategic allocation of bus stops for both students and employees). The situation is further complicated by unforeseen complications (e.g., water pollution causing disease spread), which force the player to abandon his/her ongoing task and give full attention to the new issue. The source of the problem must be evaluated while new strategies for potential solutions are weighed against proven approaches. For the current research, each patient was provided with identical settings, including a sizeable, completely functional city with a number of 2,600 residents, 50,000 game money points, and a general population satisfaction level of 90%. Their subsequent task was to boost the population of the cities to 5,000 residents while making sure that the residents were not poorly (as measured by an average satisfaction level of at least 75%) and the bank balance remained positive. On the contrary, the task was left unaccomplished if (a) the population of the urban areas dropped to 1,000, (b) the balance of the account dropped to 0, or (c) the maximum game time of 120 min had elapsed. Patients received the tip, that it was necessary to set priorities and focus on the mission.

Based on the task of raising the number of inhabitants of the city, a parameter of CPS performance was calculated as the average growth of the population relative to the target size of 5,000:

Gamers were instructed not to modify the time settings during the game, to allow for comparable measurements across participants.

Given that the participants were patients from psychiatric and psychosomatic hospitals, many of them lacked game experience. To increase test fairness between patients with different levels of game experience, all the participants were provided with a brief introduction on how to handle a list of fundamental game features:

• placement of streets, buildings, water pumps, and wind turbines;

• positioning of roads, structures, water pipes, and turbines;

• division of zones (housing, businesses, and industries/offices zones) and the mode of bulldozing;

• structural survey of power, water lines, and waste collection;

• search for the info stats to view the requirements of the residents;

Statistical analysis

For all the statistical analyses, SPSS version 26.0 (2020) was used.

On the basis of the ICD-11 definition, the personality traits were not analyzed categorically (as before), but dimensionally. To relate the expression of currently recognized personality disorders to performance in CPS, we used correlation analyses between CPS performance and the 9 scale scores of the PSSI (verified by the SCID and MMPI-2) and also the 4 overall dimensions of the BIP. Given the high number of resulting correlations, p -values could be misleading because of the multiple testing. Accordingly, we identified relevant personality traits for CPS using (i) The Bonferonni-correction of p -values and (ii) an effect sizes cut-off of r > 0.25.

In a second step, we explored, which facets of the BIP contributed to the associations with CPS performance in order to get a more fine-grained picture of possible effects.

In sum, we sought to identify the strongest predictors of CPS performance using 3 multivariate regression models with the 9 clinical traits, controlling for gender in the 2nd model and additional game experience in the 3rd model.

Table 1 lists the experience with urban planning simulation games in the current sample. About 50% of the patients rated the game as “easy” or “rather easy,” 37.5% rated it as “not easy but also not difficult” and 12.6% responded that the game was “difficult” or “very difficult.”

Table 1. Experience of the sample ( N = 242, N = 210 valid answers).

Correlation analyses show that CPS performance was negatively related to schizotypal ( r = −0.46), histrionic ( r = −0.44), and depressive ( r = −0.46) personality accentuations. The higher the expression in any of these areas, the higher the probability of failing in CPS. Effect sizes (: = r ) were > 0.40 for each of these traits (compare Table 2 ). Furthermore, CPS-performance was negatively correlated with the dependent ( r = −0.29) and paranoid ( r = −0.25) personality traits, but coefficients were much lower and therefore of less practical relevance as for schizotypical, histrionic, and depressive traits. Schizoid ( r = 0.04), borderline ( r = 0.17), anankastic ( r = −0.05), and anti-social ( r = −0.04) traits were not significantly associated with the CPS (see Table 3 ).

Table 2. Correlations of CPS and personality disorders with work-related personality manifestations as assessed with the BIP.

Table 3. Correlations between personality traits and CPS performance.

Regarding the work-related manifestations of the personality traits, CPS-performance was positively associated with the overall BIP dimensions of work orientation ( r = 0.27), professional orientation ( r = 0.34), and psychological constitution ( r = 0.25), but negatively with the overall BIP dimension social competencies ( r = −0.25). In order to explore these associations further, CPS performance and personality disorders were related to the sub-facet scores of the BIP (see Table 2 ).

Professional orientation was also negatively correlated with depressive traits ( r = −0.40), the psychological constitution was negatively correlated with borderline traits (−0.38), dependent traits (−0.31), and with depressive traits (−0.26).

The results demonstrate that particularly the facets resilience, action orientation, and motivation for creation were positively correlated with successful problem-solving, while sociability and CPS were significantly negatively correlated. The higher the resilience, action orientation and motivation for creation and the lower the sociability, the better was the CPS performance. When we take Bonferroni correction into account, also conscientiousness and motivation for leadership (italic in the table) were negatively correlated with the CPS performance.

Interestingly, the associations between personality disorders and work-related personality expressions were moderate. The strongest associations arose for resilience, which was negatively associated with several personality disorders, particularly, borderline, histrionic, and dependent traits. Focusing on the traits that showed the strongest impairment in CPS, schizotypal traits were associated with high sociability ( r = 0.36) and to a lesser extent with low-action orientation ( r = −0.22), which in turn related to low-CPS performance. Histrionic traits were related to low resilience ( r = −0.28), which in turn related to low-CPS performance. Depressive traits were related to low motivation for creation ( r = −0.25), and also low-leadership motivation ( r = −0.34) and to a lesser extent low-achievement motivation ( r = −0.21), low-action orientation ( r = −0.20), and low resilience ( r = −0.24), which in turn is related to low-CPS performance.

In a combined model with all 9 personality traits (adjusted R 2 = 36.7%), we confirmed that histrionic traits have the biggest negative impact on CPS performance (β = −0.351), followed by schizotypical (β = −0.312) and depressive traits (β = −0.303). Also, in the multiple regression model, dependent and paranoid traits are negatively related to CPS performance. If gender is the part of the model and held constant in a model containing the 9 traits, histrionic traits still have a significant and practical relevant impact of β′ = −0.325. (Condition Index = 24). The same holds true when also taking game experience into account (β″ = −0.319) see Table 4 .

Table 4. Combined regression model, β′: controlling for gender, β″ controlling for gender and game experience.

(Condition Index checking for possible multicollinearity is moderate with CI = 22, 36, so multicollinearity is moderately given, βs are, therefore, interpretable, p -values can be slightly biased, βs with 0.3 and higher found in this model for the 3 traits have for certain a significant and practically relevant impact).

The present study examined the influences of personality traits on the CPS performance in a clinical sample of individuals with a range of different psychiatric diagnoses. The aim of this empirical analysis was to extend previous research on individual differences in CPS to extreme personality traits as observed in personality disorders, and also their manifestation in work-related situations. We explored, which personality dimensions were most strongly associated with impairments in the CPS.

With regards to the clinical personality dimensions (i.e., dimensionally defined personality disorders), statistical analyses revealed that schizotypal, histrionic, dependent, and depressive personality traits were associated negatively with the participants’ performances in the given CPS task (consistent with, e.g., McMurran et al., 2007 ). Previous findings on these relationships were, therefore, further confirmed, specifically in showing that subjects with high levels of depressiveness and anxiety seemed to have more difficulties in finding and executing effective solutions to the given complex problems (e.g., see Marx et al., 1992 ; Lyubomirsky et al., 1999 ).

Unsurprisingly, no single clinical personality structure was associated with better problem-solving performances (as compared with the non-clinical trait levels). As personality disorders are generally linked with increased levels of neuroticism, which subsequently was consistently found to negatively influence problem-solving (e.g., McMurran et al., 2001 ; D’Zurilla et al., 2011 ), this result is also consistent with the general clinical intuition. But, contrary to the previous findings ( D’Zurilla et al., 2011 ), conscientiousness had no significant impact on CPS performance in this sample.

Further analyses gave deeper insights into relationships that were found in the first part of the data analyses. They are especially allowed to draw conclusions for the clinical patients. It was found that higher levels of resilience (consistent with, e.g., Garcia-Dia et al., 2013 ; Williamson et al., 2013 ; Crowther et al., 2016 , as cited in Li and Yang, 2009 ; Pinar et al., 2018 , as cited in Li et al., 2013 ), action orientation, and motivation for creation (e.g., see Eseryel et al., 2014 ) positively influenced the problem-solving performance as additional behavioral characteristics . This indicates that, even for high levels of usually negative personality traits, a person’s ability to successfully solve problems will not be impaired automatically if the person is also very resilient to the effects of negative events and highly action-oriented and motivated when facing problems. Hence, this interpretation is consistent with the conclusions of a study by Güss et al. (2017) , who found that more approach-oriented individuals outperformed avoidance-oriented participants in the complex problems. In this way, these positive traits act against the negative impact of otherwise impairing personality traits or even disorders. Interestingly, sociability was found to have a negative influence on the participants’ performances, while no significant influences on social skills, team orientation, or self-confidence were found. Therefore, it seems to be more comprehensible why some of us deal easily with complex problems and can manage things forward-looking while others do not succeed in making good decisions.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author contributions

UK was the main author, did all calculations, research to and wrote the article. SB did the programming of the microworlds and all technical support. MW did the review on the introduction and discussion part. WA and GS served as a consultant. All authors contributed to the article and approved the submitted version.

Acknowledgments

We thank Martina Mathur and Belinda Pletzer for proofreading and translating.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : complex problem solving (CPS), personality disorders, behavioral characteristics, personality traits, problem solving

Citation: Kipman U, Bartholdy S, Weiss M, Aichhorn W and Schiepek G (2022) Personality traits and complex problem solving: Personality disorders and their effects on complex problem-solving ability. Front. Psychol. 13:788402. doi: 10.3389/fpsyg.2022.788402

Received: 21 October 2021; Accepted: 08 July 2022; Published: 03 August 2022.

Reviewed by:

Copyright © 2022 Kipman, Bartholdy, Weiss, Aichhorn and Schiepek. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ulrike Kipman, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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What Is Creative Problem-Solving & Why Is It Important?

• 01 Feb 2022

One of the biggest hindrances to innovation is complacency—it can be more comfortable to do what you know than venture into the unknown. Business leaders can overcome this barrier by mobilizing creative team members and providing space to innovate.

There are several tools you can use to encourage creativity in the workplace. Creative problem-solving is one of them, which facilitates the development of innovative solutions to difficult problems.

Here’s an overview of creative problem-solving and why it’s important in business.

Access your free e-book today.

What Is Creative Problem-Solving?

Research is necessary when solving a problem. But there are situations where a problem’s specific cause is difficult to pinpoint. This can occur when there’s not enough time to narrow down the problem’s source or there are differing opinions about its root cause.

In such cases, you can use creative problem-solving , which allows you to explore potential solutions regardless of whether a problem has been defined.

Creative problem-solving is less structured than other innovation processes and encourages exploring open-ended solutions. It also focuses on developing new perspectives and fostering creativity in the workplace . Its benefits include:

• Finding creative solutions to complex problems : User research can insufficiently illustrate a situation’s complexity. While other innovation processes rely on this information, creative problem-solving can yield solutions without it.
• Adapting to change : Business is constantly changing, and business leaders need to adapt. Creative problem-solving helps overcome unforeseen challenges and find solutions to unconventional problems.
• Fueling innovation and growth : In addition to solutions, creative problem-solving can spark innovative ideas that drive company growth. These ideas can lead to new product lines, services, or a modified operations structure that improves efficiency.

Creative problem-solving is traditionally based on the following key principles :

1. Balance Divergent and Convergent Thinking

Creative problem-solving uses two primary tools to find solutions: divergence and convergence. Divergence generates ideas in response to a problem, while convergence narrows them down to a shortlist. It balances these two practices and turns ideas into concrete solutions.

2. Reframe Problems as Questions

By framing problems as questions, you shift from focusing on obstacles to solutions. This provides the freedom to brainstorm potential ideas.

3. Defer Judgment of Ideas

When brainstorming, it can be natural to reject or accept ideas right away. Yet, immediate judgments interfere with the idea generation process. Even ideas that seem implausible can turn into outstanding innovations upon further exploration and development.

4. Focus on "Yes, And" Instead of "No, But"

Using negative words like "no" discourages creative thinking. Instead, use positive language to build and maintain an environment that fosters the development of creative and innovative ideas.

Creative Problem-Solving and Design Thinking

Whereas creative problem-solving facilitates developing innovative ideas through a less structured workflow, design thinking takes a far more organized approach.

Design thinking is a human-centered, solutions-based process that fosters the ideation and development of solutions. In the online course Design Thinking and Innovation , Harvard Business School Dean Srikant Datar leverages a four-phase framework to explain design thinking.

The four stages are:

• Clarify: The clarification stage allows you to empathize with the user and identify problems. Observations and insights are informed by thorough research. Findings are then reframed as problem statements or questions.
• Ideate: Ideation is the process of coming up with innovative ideas. The divergence of ideas involved with creative problem-solving is a major focus.
• Develop: In the development stage, ideas evolve into experiments and tests. Ideas converge and are explored through prototyping and open critique.
• Implement: Implementation involves continuing to test and experiment to refine the solution and encourage its adoption.

Creative problem-solving primarily operates in the ideate phase of design thinking but can be applied to others. This is because design thinking is an iterative process that moves between the stages as ideas are generated and pursued. This is normal and encouraged, as innovation requires exploring multiple ideas.

Creative Problem-Solving Tools

While there are many useful tools in the creative problem-solving process, here are three you should know:

Creating a Problem Story

One way to innovate is by creating a story about a problem to understand how it affects users and what solutions best fit their needs. Here are the steps you need to take to use this tool properly.

1. Identify a UDP

Create a problem story to identify the undesired phenomena (UDP). For example, consider a company that produces printers that overheat. In this case, the UDP is "our printers overheat."

2. Move Forward in Time

To move forward in time, ask: “Why is this a problem?” For example, minor damage could be one result of the machines overheating. In more extreme cases, printers may catch fire. Don't be afraid to create multiple problem stories if you think of more than one UDP.

3. Move Backward in Time

To move backward in time, ask: “What caused this UDP?” If you can't identify the root problem, think about what typically causes the UDP to occur. For the overheating printers, overuse could be a cause.

Following the three-step framework above helps illustrate a clear problem story:

• The printer is overused.
• The printer overheats.
• The printer breaks down.

You can extend the problem story in either direction if you think of additional cause-and-effect relationships.

4. Break the Chains

By this point, you’ll have multiple UDP storylines. Take two that are similar and focus on breaking the chains connecting them. This can be accomplished through inversion or neutralization.

• Inversion: Inversion changes the relationship between two UDPs so the cause is the same but the effect is the opposite. For example, if the UDP is "the more X happens, the more likely Y is to happen," inversion changes the equation to "the more X happens, the less likely Y is to happen." Using the printer example, inversion would consider: "What if the more a printer is used, the less likely it’s going to overheat?" Innovation requires an open mind. Just because a solution initially seems unlikely doesn't mean it can't be pursued further or spark additional ideas.
• Neutralization: Neutralization completely eliminates the cause-and-effect relationship between X and Y. This changes the above equation to "the more or less X happens has no effect on Y." In the case of the printers, neutralization would rephrase the relationship to "the more or less a printer is used has no effect on whether it overheats."

Even if creating a problem story doesn't provide a solution, it can offer useful context to users’ problems and additional ideas to be explored. Given that divergence is one of the fundamental practices of creative problem-solving, it’s a good idea to incorporate it into each tool you use.

Brainstorming

Brainstorming is a tool that can be highly effective when guided by the iterative qualities of the design thinking process. It involves openly discussing and debating ideas and topics in a group setting. This facilitates idea generation and exploration as different team members consider the same concept from multiple perspectives.

Hosting brainstorming sessions can result in problems, such as groupthink or social loafing. To combat this, leverage a three-step brainstorming method involving divergence and convergence :

• Have each group member come up with as many ideas as possible and write them down to ensure the brainstorming session is productive.
• Continue the divergence of ideas by collectively sharing and exploring each idea as a group. The goal is to create a setting where new ideas are inspired by open discussion.
• Begin the convergence of ideas by narrowing them down to a few explorable options. There’s no "right number of ideas." Don't be afraid to consider exploring all of them, as long as you have the resources to do so.

Alternate Worlds

The alternate worlds tool is an empathetic approach to creative problem-solving. It encourages you to consider how someone in another world would approach your situation.

For example, if you’re concerned that the printers you produce overheat and catch fire, consider how a different industry would approach the problem. How would an automotive expert solve it? How would a firefighter?

Be creative as you consider and research alternate worlds. The purpose is not to nail down a solution right away but to continue the ideation process through diverging and exploring ideas.

Continue Developing Your Skills

Whether you’re an entrepreneur, marketer, or business leader, learning the ropes of design thinking can be an effective way to build your skills and foster creativity and innovation in any setting.

If you're ready to develop your design thinking and creative problem-solving skills, explore Design Thinking and Innovation , one of our online entrepreneurship and innovation courses. If you aren't sure which course is the right fit, download our free course flowchart to determine which best aligns with your goals.

About the Author

What Are Problem-Solving Skills and Why Do They Matter?

Problem-solving skills are the ability to identify challenges, analyze them, and develop effective solutions. These skills are crucial not only in professional environments but also in everyday life situations. Whether it’s overcoming obstacles at work, resolving conflicts in relationships, or tackling complex issues, honing problem-solving skills can lead to more efficient and successful outcomes.

Understanding the Essence of Problem-Solving Skills

At the core of problem-solving skills lies a set of cognitive processes that enable individuals to navigate through challenges. These processes include critical thinking, creativity, decision-making, and analytical reasoning. Problem-solving also involves various approaches, such as trial and error, deductive reasoning, and lateral thinking.

The Significance of Problem-Solving Skills

In both personal and professional contexts, individuals with strong problem-solving abilities tend to excel. They can adapt to changing circumstances, make informed decisions, and resolve conflicts effectively. Employers value employees who can identify problems, propose solutions, and implement them efficiently, contributing to organizational success.

Vati is your ultimate career planning and assessment platform , designed to empower individuals in navigating their professional journey with confidence. for developing essential problem-solving skills. Through interactive modules, real-world scenarios, and expert guidance, Vati empowers users to enhance their critical thinking, decision-making, and analytical abilities. Whether in personal or professional settings, Vati equips individuals with the tools needed to tackle challenges with confidence and efficiency.

Vati offers insights, examples, and techniques to empower individuals in navigating life’s challenges effectively and efficiently.

How to Enhance Your Problem-Solving Skills

Improving problem-solving skills is a continuous process that involves several steps:

• Identify the Problem: Clearly define the issue or challenge you are facing.
• Gather Relevant Information: Collect data and facts necessary to understand the problem fully.
• Generate Potential Solutions: Brainstorm various approaches or strategies to address the problem.
• Evaluate and Select the Best Solution: Assess the pros and cons of each solution and choose the most suitable one.
• Implement the Solution: Put your chosen solution into action, considering practical constraints and resources.
• Reflect on the Outcome: Analyze the results of your actions and identify lessons learned for future reference.

Practical Strategies for Improving Problem-Solving Skills

To enhance your problem-solving abilities, consider the following strategies:

• Practice Critical Thinking : Engage in activities that require logical reasoning and analysis.
• Engage in Brainstorming Sessions: Collaborate with others to generate creative solutions to complex problems.
• Seek Feedback and Learn from Mistakes: Embrace constructive criticism and use failures as opportunities for growth.
• Embrace Challenges and Setbacks: View obstacles as learning opportunities and remain resilient in the face of adversity.

Examples of Problem-Solving Skills in Action

In the workplace, problem-solving skills are demonstrated through:

• Resolving conflicts among team members.
• Developing innovative solutions to increase efficiency.
• Handling customer complaints and finding satisfactory resolutions.

In everyday life, problem-solving skills are showcased when:

• Planning and organizing tasks to meet deadlines.
• Negotiating compromises in interpersonal relationships.
• Finding alternative routes to reach a destination during unexpected road closures.

What are the 7 Problem-Solving Techniques?

• Define the Problem: Clearly articulate the issue or challenge you are facing.
• Brainstorm Solutions: Generate as many potential solutions as possible without judgment.
• Evaluate Options: Assess the feasibility and effectiveness of each solution.
• Choose the Best Solution: Select the most suitable option based on the evaluation.
• Implement the Solution: Put the chosen solution into action.
• Monitor Progress: Track the implementation process and make adjustments as needed.
• Reflect and Learn from the Process: Analyze the outcomes and identify areas for improvement.

In conclusion, problem-solving skills are indispensable assets that empower individuals to navigate through life’s challenges with confidence and efficiency. By honing these skills through practice, reflection, and continuous learning , individuals can unlock their full potential and achieve success in various aspects of their lives.

FAQs (Frequently Asked Questions)

1. How can problem-solving skills benefit me in my career?

Problem-solving skills are highly valued by employers as they enable individuals to tackle complex challenges, make informed decisions, and contribute to organizational success.

2. Can problem-solving skills be learned, or are they innate?

While some people may have a natural inclination towards problem-solving, these skills can be developed and refined through practice, experience, and learning from mistakes.

3. What role does creativity play in problem-solving?

Creativity is essential in problem-solving as it allows individuals to think outside the box, generate innovative solutions, and approach challenges from different perspectives.

4. How can I assess my problem-solving skills?

You can assess your problem-solving skills by reflecting on past experiences, seeking feedback from others, and actively engaging in problem-solving activities.

5. Are there specific industries where problem-solving skills are particularly crucial?

Problem-solving skills are valuable in virtually every industry, including business, healthcare, education, technology, and government, as they enable individuals to address diverse challenges and seize opportunities for growth and innovation.

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What are analytical skills? Examples and how to level up

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What are analytical skills?

Why are analytical skills important, 9 analytical skills examples, how to improve analytical skills, how to show analytical skills in a job application, the benefits of an analytical mind.

With market forecasts, performance metrics, and KPIs, work throws a lot of information at you. 

If you want to stay ahead of the curve, not only do you have to make sense of the data that comes your way — you need to put it to good use. And that requires analytical skills.

You likely use analytical thinking skills every day without realizing it, like when you solve complex problems or prioritize tasks . But understanding the meaning of analysis skills in a job description, why you should include them in your professional development plan, and what makes them vital to every position can help advance your career.

Analytical skills, or analysis skills, are the ones you use to research and interpret information. Although you might associate them with data analysis, they help you think critically about an issue, make decisions , and solve problems in any context. That means anytime you’re brainstorming for a solution or reviewing a project that didn’t go smoothly, you’re analyzing information to find a conclusion. With so many applications, they’re relevant for nearly every job, making them a must-have on your resume.

Analytical skills help you think objectively about information and come to informed conclusions. Positions that consider these skills the most essential qualification grew by 92% between 1980 and 2018 , which shows just how in-demand they are. And according to Statista, global data creation will grow to more than 180 zettabytes by 2025 — a number with 21 zeros. That data informs every industry, from tech to marketing.

Even if you don’t interact with statistics and data on the job, you still need analytical skills to be successful. They’re incredibly valuable because:

• They’re transferable: You can use analysis skills in a variety of professional contexts and in different areas of your life, like making major decisions as a family or setting better long-term personal goals.
• They build agility: Whether you’re starting a new position or experiencing a workplace shift, analysis helps you understand and adapt quickly to changing conditions. 
• They foster innovation: Analytical skills can help you troubleshoot processes or operational improvements that increase productivity and profitability.
• They make you an attractive candidate: Companies are always looking for future leaders who can build company value. Developing a strong analytical skill set shows potential employers that you’re an intelligent, growth-oriented candidate.

If the thought of evaluating data feels unintuitive, or if math and statistics aren’t your strong suits, don’t stress. Many examples of analytical thinking skills don’t involve numbers. You can build your logic and analysis abilities through a variety of capacities, such as:

1. Brainstorming

Using the information in front of you to generate new ideas is a valuable transferable skill that helps you innovate at work . Developing your brainstorming techniques leads to better collaboration and organizational growth, whether you’re thinking of team bonding activities or troubleshooting a project roadblock. Related skills include benchmarking, diagnosis, and judgment to adequately assess situations and find solutions.

2. Communication

Becoming proficient at analysis is one thing, but you should also know how to communicate your findings to your audience — especially if they don’t have the same context or experience as you. Strong communication skills like public speaking , active listening , and storytelling can help you strategize the best ways to get the message out and collaborate with your team . And thinking critically about how to approach difficult conversations or persuade someone to see your point relies on these skills. 

3. Creativity

You might not associate analysis with your creativity skills, but if you want to find an innovative approach to an age-old problem, you’ll need to combine data with creative thinking . This can help you establish effective metrics, spot trends others miss, and see why the most obvious answer to a problem isn’t always the best. Skills that can help you to think outside the box include strategic planning, collaboration, and integration.

4. Critical thinking

Processing information and determining what’s valuable requires critical thinking skills . They help you avoid the cognitive biases that prevent innovation and growth, allowing you to see things as they really are and understand their relevance. Essential skills to turn yourself into a critical thinker are comparative analysis, business intelligence, and inference.

5. Data analytics

When it comes to large volumes of information, a skilled analytical thinker can sort the beneficial from the irrelevant. Data skills give you the tools to identify trends and patterns and visualize outcomes before they impact an organization or project’s performance. Some of the most common skills you can develop are prescriptive analysis and return on investment (ROI) analysis.

6. Forecasting

Predicting future business, market, and cultural trends better positions your organization to take advantage of new opportunities or prepare for downturns. Business forecasting requires a mix of research skills and predictive abilities, like statistical analysis and data visualization, and the ability to present your findings clearly.

7. Logical reasoning

Becoming a logical thinker means learning to observe and analyze situations to draw rational and objective conclusions. With logic, you can evaluate available facts, identify patterns or correlations, and use them to improve decision-making outcomes. If you’re looking to improve in this area, consider developing inductive and deductive reasoning skills.

8. Problem-solving

Problem-solving appears in all facets of your life — not just work. Effectively finding solutions to any issue takes analysis and logic, and you also need to take initiative with clear action plans . To improve your problem-solving skills , invest in developing visualization , collaboration, and goal-setting skills.

9. Research

Knowing how to locate information is just as valuable as understanding what to do with it. With research skills, you’ll recognize and collect data relevant to the problem you’re trying to solve or the initiative you’re trying to start. You can improve these skills by learning about data collection techniques, accuracy evaluation, and metrics.

You don’t need to earn a degree in data science to develop these skills. All it takes is time, practice, and commitment. Everything from work experience to hobbies can help you learn new things and make progress. Try a few of these ideas and stick with the ones you enjoy:

1. Document your skill set

The next time you encounter a problem and need to find solutions, take time to assess your process. Ask yourself:

• What facts are you considering?
• Do you ask for help or research on your own? What are your sources of advice?
• What does your brainstorming process look like?
• How do you make and execute a final decision?
• Do you reflect on the outcomes of your choices to identify lessons and opportunities for improvement?
• Are there any mistakes you find yourself making repeatedly?
• What problems do you constantly solve easily? 

These questions can give insight into your analytical strengths and weaknesses and point you toward opportunities for growth.

2. Take courses

Many online and in-person courses can expand your logical thinking and analysis skills. They don’t necessarily have to involve information sciences. Just choose something that trains your brain and fills in your skills gaps . 

Consider studying philosophy to learn how to develop your arguments or public speaking to better communicate the results of your research. You could also work on your hard skills with tools like Microsoft Excel and learn how to crunch numbers effectively. Whatever you choose, you can explore different online courses or certification programs to upskill. 

3. Analyze everything

Spend time consciously and critically evaluating everything — your surroundings, work processes, and even the way you interact with others. Integrating analysis into your day-to-day helps you practice. The analytical part of your brain is like a muscle, and the more you use it, the stronger it’ll become. 

After reading a book, listening to a podcast, or watching a movie, take some time to analyze what you watched. What were the messages? What did you learn? How was it delivered? Taking this approach to media will help you apply it to other scenarios in your life. 

If you’re giving a presentation at work or helping your team upskill , use the opportunity to flex the analytical side of your brain. For effective teaching, you’ll need to process and analyze the topic thoroughly, which requires skills like logic and communication. You also have to analyze others’ learning styles and adjust your teachings to match them. 

5. Play games

Spend your commute or weekends working on your skills in a way you enjoy. Try doing logic games like Sudoku and crossword puzzles during work breaks to foster critical thinking. And you can also integrate analytical skills into your existing hobbies. According to researcher Rakesh Ghildiyal, even team sports like soccer or hockey will stretch your capacity for analysis and strategic thinking . 

6. Ask questions

According to a study in Tr ends in Cognitive Sciences, being curious improves cognitive function , helping you develop problem-solving skills, retention, and memory. Start speaking up in meetings and questioning the why and how of different decisions around you. You’ll think more critically and even help your team find breakthrough solutions they otherwise wouldn’t.

7.Seek advice

If you’re unsure what analytical skills you need to develop, try asking your manager or colleagues for feedback . Their outside perspective offers insight you might not find within, like patterns in. And if you’re looking for more consistent guidance, talking to a coach can help you spot weaknesses and set goals for the long term.

8. Pursue opportunities

Speak to your manager about participating in special projects that could help you develop and flex your skills. If you’d like to learn about SEO or market research, ask to shadow someone in the ecommerce or marketing departments. If you’re interested in business forecasting, talk to the data analysis team. Taking initiative demonstrates a desire to learn and shows leadership that you’re eager to grow. 

Shining a spotlight on your analytical skills can help you at any stage of your job search. But since they take many forms, it’s best to be specific and show potential employers exactly why and how they make you a better candidate. Here are a few ways you can showcase them to the fullest:

1. In your cover letter

Your cover letter crafts a narrative around your skills and work experience. Use it to tell a story about how you put your analytical skills to use to solve a problem or improve workflow. Make sure to include concrete details to explain your thought process and solution — just keep it concise. Relate it back to the job description to show the hiring manager or recruiter you have the qualifications necessary to succeed.

2. On your resume

Depending on the type of resume you’re writing, there are many opportunities to convey your analytical skills to a potential employer. You could include them in sections like: 

• Professional summary: If you decide to include a summary, describe yourself as an analytical person or a problem-solver, whichever relates best to the job posting. 
• Work experience: Describe all the ways your skill for analysis has helped you perform or go above and beyond your responsibilities. Be sure to include specific details about challenges and outcomes related to the role you’re applying for to show how you use those skills. 
• Skills section: If your resume has a skill-specific section, itemize the analytical abilities you’ve developed over your career. These can include hard analytical skills like predictive modeling as well as interpersonal skills like communication.

3. During a job interview

As part of your interview preparation , list your professional accomplishments and the skills that helped along the way, such as problem-solving, data literacy, or strategic thinking. Then, pull them together into confident answers to common interview questions using the STAR method to give the interviewer a holistic picture of your skill set.

Developing analytical skills isn’t only helpful in the workplace. It’s essential to life. You’ll use them daily whenever you read the news, make a major purchase, or interact with others. Learning to critically evaluate information can benefit your relationships and help you feel more confident in your decisions, whether you’re weighing your personal budget or making a big career change .

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Elizabeth Perry is a Coach Community Manager at BetterUp. She uses strategic engagement strategies to cultivate a learning community across a global network of Coaches through in-person and virtual experiences, technology-enabled platforms, and strategic coaching industry partnerships. With over 3 years of coaching experience and a certification in transformative leadership and life coaching from Sofia University, Elizabeth leverages transpersonal psychology expertise to help coaches and clients gain awareness of their behavioral and thought patterns, discover their purpose and passions, and elevate their potential. She is a lifelong student of psychology, personal growth, and human potential as well as an ICF-certified ACC transpersonal life and leadership Coach.

20 examples of development opportunities that can level up your career

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Assessing and Teaching 21st Century Skills: Collaborative Problem Solving as a Case Study

• First Online: 05 April 2017

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• Patrick Griffin 6  

Part of the book series: Methodology of Educational Measurement and Assessment ((MEMA))

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This chapter describes the assessment of collaborative problem solving using human-to-human interaction. Tasks were designed to require partners to contribute resources or skills that they uniquely controlled. Issues were task design, data capture, item and data definition, calibration, and the link to teaching intervention. The interpretation of the student performance is mapped to a criterion-referenced interpretation framework, and reports are designed to assist teachers to intervene at a Vygotsky zone of proximal development in order to promote development of the student ability in collaborative problem solving. The data analytics demonstrate how the equivalent of test items are developed and issues such a local independence are discussed.

An earlier version of this chapter was presented as a keynote lecture at the Institute of Curriculum & Instruction at East China Normal University, November 6–8, 2015, Shanghai, China.

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Griffin, P. (2017). Assessing and Teaching 21st Century Skills: Collaborative Problem Solving as a Case Study. In: von Davier, A., Zhu, M., Kyllonen, P. (eds) Innovative Assessment of Collaboration. Methodology of Educational Measurement and Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-33261-1_8

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9 Transferable Skills That Can Help You in Life

Learn about transferable skills and why they are so important..

Posted June 3, 2024 | Reviewed by Gary Drevitch

Cowritten by Nathalie Boutros and Tchiki Davis.

The average person can expect to hold 12 different jobs in his or her lifetime (United States Bureau of Labor Statistics, 2021). With all this job-changing, how can you navigate your career in the direction that you want to take it? One way to improve your career prospects may be to cultivate your transferable skills.

Transferable skills are those skills that are useful, and maybe even necessary, to the performance of a wide variety of jobs. A skill may be considered transferable if you learn and perfect it in one context, like school, a job, volunteer work, or a hobby, and then can use that skill in new and different situations (Nagele & Stalder, 2017). A huge range of skills, proficiencies, competencies, and talents may qualify as transferable skills. Some transferable skills are very specific and technical—for example, knowledge of specific software or industry regulations. Other transferable skills are more generic such as a general proficiency with computers, or fluency in a foreign language. A third category of transferable skills is often called “soft skills," such as the ability to communicate effectively and problem-solve creatively.

Soft skills are a type of transferable skills that are often needed to successfully apply technical skills and knowledge (Bancino & Zevalkink, 2007). For example, a restaurant manager’s ability to create a work schedule for a large staff requires technical skills like numeracy, literacy, computer proficiency, and administrative skills. Creating a schedule that staff members are generally happy with also requires the soft skills of empathy, leadership , and listening. ( Learn more about some of your soft skills through this well-being quiz.)

Why Transferable Skills Are Important

Change is an increasingly large part of people’s professional lives. Even within the same job, you may often change teams or projects. Having skills that transfer from one situation to another may be extremely helpful when adapting to these frequent changes in your roles and responsibilities.

While technical skills that are readily transferable across contexts may serve you well, having soft skills such as ambiguity tolerance, cultural acceptance, self-confidence , creative thinking , and the ability to give and receive feedback may be particularly valuable (de Villiers, 2010). Having a set of soft skills that you can carry from one role to another may even improve your earning potential. People with the soft skills of leadership, planning, and problem-solving tend to have higher incomes (Ramos et al., 2013).

Examples of Transferable Skills

Skills and proficiencies that tend to be important across workplace settings include (Nagele & Stalder, 2017):

• Fundamental skills – literacy, numeracy, proficiency with technology, and physical skills.
• People skills – oral and written communication, interpersonal skills, influencing, negotiating, teamwork , customer service, leadership, and management.
• Conceptualizing or thinking skills – managing information, problem-solving, organizing and planning, critical thinking, systems thinking, time-management, and teachability.
• Business skills – innovation, entrepreneurship, and administrative skills.
• Community skills – citizenship, work ethic, emotional labor , cultural awareness, and expression.

Although skills from each category may be required to do most jobs, the specific skills needed to perform a specific job may vary. Some transferable skills are more general than others. For example, basic communication and literacy skills will probably be required in most jobs. Other transferable skills may not be valued in as many jobs or industries. For example, customer service skills may not be as strongly valued in manufacturing roles as they are in cashier roles.

Transferable skills can be organized into broad categories of specific competencies and strengths (Ramos et al., 2013). Describing your specific abilities may be more informative than making broad statements about your generic skills.

• Literacy Skills – reading and writing documents, memos, forms, or reports.
• Leadership Skills – coaching and motivating staff, developing staff competencies, planning activities, making strategic decisions, and managing resources.
• Physical Skills – physical strength, dexterity with your hands, endurance, and stamina.
• Problem Solving Skills – spotting and analyzing problems, identifying causes, and finding solutions.
• Influencing Skills – advising customers, persuading others, dealing with people, making speeches and presentations.
• Teamwork Skills – working in teams, listening to colleagues, paying attention to details.
• Planning Skills – time-management, organizing, and planning tasks.
• Numeracy Skills – working with numbers or using advanced mathematical and statistical tools.
• Emotional Labor – language skills, negotiation, emotion -regulation, and managing other people's feelings.

Transferring your skills from one situation to another may not be easy (Saks et al., 2014). The ability to recognize which of your skills may serve you well in a new situation is itself a skill. And recognizing which of your skills are transferable and what new skills you may need to pursue may be the most valuable transferable skill of all.

Adapted from a post on transferable skills published by The Berkeley Well-Being Institute.

Tchiki Davis, Ph.D. , is a consultant, writer, and expert on well-being technology.

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Cognitive control, intentions, and problem solving in skill learning

Wayne christensen.

1 Philosophy, University of Barcelona, Barcelona, Spain

Kath Bicknell

2 School of Social Sciences and School of Psychological Sciences, Macquarie University, Sydney, Australia

We investigate flexibility and problem solving in skilled action. We conducted a field study of mountain bike riding that required a learner rider to cope with major changes in technique and equipment. Our results indicate that relatively inexperienced individuals can be capable of fairly complex 'on-the-fly' problem solving which allows them to cope with new conditions. This problem solving is hard to explain for classical theories of skill because the adjustments are too large to be achieved by automatic mechanisms and too complex and rapid to be achieved by cognitive processes as they are usually understood. A recent theory, Mesh, can explain these results because it posits that skill-specific cognitive abilities develop during skill learning and that control typically involves an interplay between cognitive and automatic mechanisms. Here we develop Mesh further, providing a detailed explanation for these problem solving abilities. We argue that causal representation, metacognitive awareness and other forms of performance awareness combine in the formulation and control of action strategies. We also argue that the structure of control present in this case is inconsistent with Bratman's model of intentions, and that, in the face of high uncertainty and risk, intentions can be much more labile than Bratman recognises. In addition, we found limitations and flaws in problem solving which illuminate the representations involved. Finally, we highlight the crucial role of social and cultural learning in the development of complex skills.

Introduction

What is learned during skill learning? What role does cognitive control—the form of control involved in flexible, goal-directed thought and action—play in the learning process? Classical skill theories, such as those of Fitts and Posner ( 1967 ) and Anderson ( 1982 ), treat cognitive control as responsible for discovering the structure of the actions that the skill requires, and for their initial implementation, but as being supplanted by more efficient automatic processes as learning progresses. Most work on skill focuses on the development of automaticity and the abilities and mechanisms that automatically-produced skills might involve. But humans show an exceptionally high degree of flexibility in skilled action, including forms of flexibility that rely on problem solving to construct solutions, as opposed to the deployment of pre-learned solutions. This kind of flexibility has not received much attention, even though it is arguably the critical ability underlying the richness and diversity of human skill.

The classical view sees skill as automating because it regards cognitive control as fundamentally unsuited to the demands of skilled action control. Cognitive control is thought to be slow, serial, and as having limited capacity, whereas automatic processes are fast, parallel, and have high capacity (Shiffrin & Schneider, 1977 ; Evans & Stanovich, 2013 ). Cognitive control uses highly generalised representations and problem solving methods which are an inefficient means for producing the specialised responses of skill (Anderson, 1982 ). In other words, cognitive control is specialised for reasoning, not action control, and it is a clumsy tool to use for action control. But it's questionable whether cognitive control is really fundamentally unsuited to skilled action control, as assumed by the classical view. Certainly, early cognitively-driven efforts to perform a skill are clumsy, and working memory is often overloaded by task demands. But significant degrees of fluency emerge long before strong automaticity could be in place. Skill research tends to focus on motor skill in particular, but if we consider expertise research more generally it is clear that experts can acquire domain-specific cognitive skills which can allow them to rapidly process large amounts of information (Ericsson & Kintsch, 1995 ) and engage in powerful, domain-specific forms of problem solving (Chi et al., 1982 ). There is no obvious reason why skills that involve a strong motor component might not also incorporate cognitive abilities involved in control and problem solving.

A recent skill theory called Mesh proposes just this (Christensen & Sutton, 2018 ; Christensen et al., 2016 , 2019 ). It claims that almost all skills incorporate an important cognitive component, including those which are paradigmatically motoric like golf putting. Skilled performance is produced by meshed cognitive and automatic processes which are generated by the cooperative operation of many neural systems. Cognitive processes provide flexibility by shaping action to the context and by solving the problems that complex, variable environments and tasks present. This paper extends Mesh by investigating the nature of skilled problem solving more closely. We conducted a field study investigating adjustments to major task changes by a rider with several years mountain bike riding experience (and many years of road riding experience) but beginner-level mountain bike technique. This was one of the authors, Wayne. Kath, who is a highly experienced rider, provided instruction. Guidance from Kath prompted major changes in Wayne's riding technique. A change in bike during the field study also encouraged significant adjustments in Wayne’s riding.

Because Wayne was not a raw novice he possessed mountain-biking-specific problem solving abilities which allowed him to cope with these large changes more fluently than might be expected based on the classical view. His experiences navigating rocky descents and ascents, and challenging log roll-overs, help to illuminate the nature of the control involved in this kind of problem solving. These problems involve high uncertainty and significant risk, and control processes flexibly adjust action to manage this risk and uncertainty. The learner is learning the structure of the problem as they try out solutions, and the strategy is monitored, modified and sometimes abandoned during execution. Evaluation is complex, employing a rich set of criteria and flexible holistic assessment. To understand these features of control we need a more expansive concept of control and a more labile picture of intentions than the standard picture recognises (Bratman, 1987 ).

The problem solving that Wayne engaged in also helps to illuminate the nature of the representations involved. Recently there has been considerable interest in characterising the type of the representations that are required for the ability to perform actions (Pacherie, 2011 ). Most accounts have focused on representations of action form , with an emphasis on schemas (e.g. Mylopoulos & Pacherie, 2018 ; Fridland, 2021 ). In contrast, some accounts have focused on the representation of causal structure (Christensen et al., 2015 ; Goldenberg, 2013 ).

Here we endorse the view that causal representation plays an important role, and show how this kind of representation supports flexibility. We suggest that Wayne employed causal representation to identify the structure of the problems he faced and formulate solutions. Because he could understand the causal significance of some of the features of the altered technique and equipment he could rapidly formulate new strategies to cope with and exploit the changes. But Wayne's problem solving abilities showed strong limitations which are also revealing. Wayne failed to properly implement a key riding technique even though he understood the technique abstractly and thought he was implementing it. The problem was diagnosed by Kath, who corrected his implementation. His difficulty involved a failure to properly map between an abstract representation of the technique and the representations used in the control of execution. The latter have particular a type of content, in particular a systematic representation of the state space of execution control . They also have a characteristic perspective that we call the perspective of execution control . This perspective is related to, though distinct from, the type of perspective described by (Pavese, 2019 ) in her concept of practical modes of presentation.

In Sect.  2 we present the theoretical context for the study and describe its conception. In Sect.  3 we give an overview of the activities we conducted. Section  4 then analyses these experiences in close detail using the theoretical ideas developed in Sect.  2 .

Context and approach

This paper centers on a field study which is analysed for its theoretical implications. In both philosophy and psychology this is an unusual method which requires some explanation and defense. In a separate paper, Christensen (in preparation) develops a general argument that cognitive ecology should be a central discipline in psychology, that ecological methods should be incorporated into philosophy just as experimental methods have been, and that the present lack of attention to cognitive ecology is a serious limitation on the development of deep theory in both psychology and philosophy of mind (see also Bicknell & Sutton, 2022 ). Here we focus on a more restricted argument which highlights some of the ways that the theoretical issues we are concerned with in this paper are sensitive to ecological data.

As noted, we are concerned with skill learning, and with the role of cognitive control, intentions, and action strategies in skill learning. These are evolved adaptive traits whose structure and function are shaped by a complex mixture of evolutionary adaptation and learning. It follows that understanding the nature of the adaptive functions and mechanisms depends critically on understanding the ecological problems they respond to. Consequently, we need to investigate these phenomena in the context of the ecological problems they are adapted to solve. This requires the use of ecological methods to investigate the nature of these problems. We currently lack a detailed understanding of these ecological problems.

Bratman's ( 1987 ) theory of intentions is an especially relevant case where theory is sensitive to the details of these ecological problems. 1 Bratman’s account is widely accepted and is used as a framework by numerous contemporary researchers. Mylopoulos and Pacherie’s ( 2018 ) account of intentional action control is an example which we will compare with our own model. Bratman characterises intentions based on an analysis of human planning behavior. In developing his theory Bratman focuses primarily on "ordinary, humdrum cases in which future-directed intentions and partial plans lead without great difficulty from prior deliberation to later conduct" (p. 12). He sees these kinds of cases as contrasting with more complex cases that involve difficulties of self-control. He writes,

Such examples are quite fascinating. But I think we get a distorted view of future-directed intention if we take them as paradigmatic of intention. It is best, I think, to begin with ordinary, garden-variety cases in which, without major psychological resistance, future-directed intentions and partial plans support coordination in the lives of limited agents like us. It is here that we need to look to get at the major regularities, roles, and norms in terms of which we can understand intention and its associated commitment . (p. 12, emphasis added.)

In this passage Bratman shows an awareness of the importance of the ecological representativeness of types of cases. However, Bratman's ecological picture is itself flawed. Bratman appears to assume that ordinary humdrum cases “without great difficulty” are ecologically typical or predominant while cases with major psychological conflict are exceptional. He also assumes that these are the only significant variations to be considered. Bratman's examples are indeed, for the most part, mundane, such as going to the library to borrow a book, or deciding whether to have a milkshake for lunch. These cases involve low stakes, high information, low complexity, context stability, and low time pressure. Only one of his examples, a presidential TV debate, involves high stakes, high complexity and high time pressure. Bratman uses the complexity and time pressure of the case in his analysis but he doesn't systematically examine these features as such or consider the significance of variation in them for his theory. All these features vary greatly across naturally occurring human ecological contexts. And crucially, these variations have important theoretical implications. The basic structure of Bratman’s model of intention involves a phase of deliberation in which an intention is formed, followed by a phase in which evaluation of the intention has ceased and cognitive processes are devoted to implementation. Intentions serve as fixed anchor points which structure cognition and behavior after they have been adopted. But this model may be best suited to cases that involve low stakes and high information. In cases with significant uncertainty and high stakes we might expect evaluation of the intention to be ongoing during execution as information comes in. In some kinds of cases it is possible to abort an action part way through or change its fundamental nature. Accordingly, in these kinds of contexts intentions may be relatively labile, being evaluated, modified, and sometimes abandoned during execution as information about the action is acquired. Skill learning is an important context in which this kind of pattern is likely.

Lack of ecological data is also a problem for skill theory. Classical skill theories, such as those of Fitts and Posner ( 1967 ) and Dreyfus and Dreyfus ( 1986 ), propose that skill acquisition has a well-defined stage structure culminating in a final stage of full automaticity. Fitts and Posner's model has just three stages, while Dreyfus and Dreyfus's account has five. Yet skills are extremely diverse, ranging from the ability to play tic-tac-toe to being a concert pianist or astrophysicist. It is unlikely that a simple three or five stage model provides a satisfactory fit to all the skill acquisition pathways involved in developing such diverse expert abilities. In athletics, high jumping has a standardised, invariant task structure, and a single action strategy, the Fosbury Flop, has dominated since the late 1960s. In contrast, in competition bouldering route setters set highly diverse novel climbing problems at each competition. Climbers are allowed only four minutes to inspect the wall and formulate strategies. Climbers try out varied strategies, often employing strategies which suit their individual abilities and physical characteristics, such as explosive strength, limb and hand size, or flexibility and balance. 2 Multiple strategies may succeed on a given problem, including strategies not anticipated by the route setters. 3 At odds with classical skill theories, climbers and commentators often describe bouldering at elite levels as highly cognitive. This is less often said about high jump. To get a better understanding of skill we need a fuller picture of this diversity.

In this paper we take a small step towards filling out this picture. Our objective is to bring detailed theoretical analysis into a close engagement with detailed empirical ecological reporting and analysis. While our sample is a tiny slice of the big picture, close analysis reveals that it has features with wide-ranging theoretical significance.

Ecological context

The ecological context we have focused on is that of socially-guided learning of a complex, fast-paced skill in a variable, physically demanding environment. Specifically, we have focused on the problems of coping with major changes in mountain biking technique and equipment in an individual at a relatively early stage of skill development, with some experience but beginner-level technique. The ability to acquire complex, fast-paced sensorimotor skills is highly developed in primates, with arboreal lifestyle being a primary ecological basis for this. Primates show high levels of behavioral flexibility, manifested in foraging strategies, communication, social behavior and tool use. Amongst primates, humans are exceptional in showing an extremely highly developed capacity for flexible skill acquisition. This plays a fundamental role in human lifestyles. Human skills tend to be highly social, often acquired through social learning and exercised in social contexts.

Cognitive control plays a central role in learning complex novel skills. It is accordingly likely that in human evolution there has been selection on the capacity for cognitive control for functions that contribute to complex skill learning. Some contributions of cognitive control are probably not specific adaptations for skill learning, but rather more general abilities that contribute to skill learning amongst other important adaptive human capacities. Conversely, skill learning is likely to shape the mechanisms of cognitive control generally, both via selection and activity-dependent plasticity. During skill learning, new capacities for cognitive control are acquired, so some of the capacities and mechanisms involved in cognitive control may be more apparent (see for example, Bicknell, 2021 ; Bicknell & Brümmer, 2022 ; Downey, 2022 ).

Our rationale for focusing on the ability to cope with major changes in technique and equipment is that this is a demanding context which should illuminate mechanisms for control of performance and skill learning.

Testing the classical procedural-cognitive contrast

We can make an initial framing in terms of the standard distinction between implicit and explicit processes. Broadly, the kind of procedural or implicit learning usually associated with skill acquisition occurs slowly and incrementally, and the abilities which result are relatively inflexible (Reber et al., 2019 ). These mechanisms should hence be unable to respond to large, rapid changes. In addition, the classical view of skill claims that cognitive processes are ill-suited to the control of skilled action. Cognitive control processes should therefore struggle to cope with major novel changes to the way a task is performed. In contrast, Mesh theory claims that cognitive control of action improves with skill learning and incorporates several features which allow relatively efficient control, including representation of the causal structure of action problems and metacognitive and other kinds of performance awareness (Christensen et al., 2015 , 2019 ). It is consequently better placed to explain relatively fluent adjustments to major novel changes if, as we expected, these do occur.

Forms of flexibility

We also need a more fine-grained framing of the rationale for looking at large changes in technique and equipment. This is because numerous motor control mechanisms have been proposed which are capable of flexibility in various forms. In the situations we're concerned with it is plausible that multiple forms of flexibility play important roles.

One of the simplest forms of flexible control is feedback control. Here, feedback corrects deviation from a reference. The goal can be achieved from any point in a state space 'basin' defined by the abilities to detect deviations and produce control inputs which drive the system towards the goal state. Trace theory (Adams, 1971 ) and the control law model (Fajen, 2005 ; Gibson, 1979 ) are theories of skilled action production based on feedback. Another class of control system achieves greater flexibility by means of generalised sensorimotor mappings. Theories of this type include Schmidt's ( 1975 ) schema theory , the internal models approach (Daniel M. Wolpert & Kawato, 1998 ; Wolpert et al., 1995 ), Optimal Feedback Control theory (Todorov, 2004 ), and the coordinative structures of dynamical systems theory (e.g. Kelso & Zanone, 2002), which generate high order patterns in movement. The perception-motor mappings are generalised in the sense that they generalise from practiced to unpracticed contexts based on similarity.

Calibration is a form of flexibility in which the parameters of a control function are adjusted to suit the context. A different kind of flexibility is achieved by restricting regulation only to variables that affect goal-achievement (Todorov, 2004 ; Tseng et al., 2003 ). Restricting control only to variables that affect goal-achievement is resource-efficient and can have the effect of decoupling variables important for the goal from those which aren't, which buffers performance against variations in unimportant variables. Yet another kind of flexibility involves control of the way a strategy is executed. Impedance control , or the control of the stiffness of the motor system, is an example (Franklin et al., 2008 ). Thus, a given action type can be performed while maintaining varying degrees and forms of stiffness. Control of stiffness can have a variety of functional benefits. For instance, increasing stiffness can reduce the degrees of freedom present in a movement and hence simplify the movement problem, while reducing stiffness can reduce the negative consequences of impacts that arise as a result of errors.

A key form of flexibility, sometimes called equivalence , involves the ability to achieve a given goal using multiple qualitatively distinct movement patterns. Ranganathan et al. ( 2020 ) identify two kinds of mechanism capable of this kind of flexibility. The first type involve high order task-specific coordination functions which constrain the dynamics of the system in a way that allows multiple coordination modes, or synergies . The second consist of explicit strategies (Christensen & Bicknell, 2019 ; Christensen et al., 2015 , 2019 ; Shepherd, 2017 ; Taylor & Ivry, 2012 ). Ranganathan et al. ( 2020 ) suggest that flexibility is likely to be based on synergies when the variations in movement pattern are relatively small and the task constraints can be learned over a long period of time. Explicit strategies are likely to be used when the variations in movement patterns are large and the task constraints change over short time scales. The situation we are examining has these features so it should evoke the use of strategies.

Problem solving

The key question that then arises with regard to action strategies is how they are formulated and controlled using cognitive processes. As we saw, according to the classical view (e.g. Anderson, 1982 ) cognitive control includes no specialisations for action control. An alternative view, adopted in Mesh, is that control of action is one of the primary functions performed by cognitive control, and it incorporates mechanisms acquired through evolution and learning that are specialised for this role. These mechanisms engage in problem solving processes which represent the structure of action problems and construct solution strategies. Cognitive control then governs the implementation of these strategies.

Problem solving is a relatively understudied issue in motor control research, which is surprising on ecological grounds given the high degree of diversity and flexibility of human motor abilities, and the importance of flexible motor abilities in human evolution. Bernstein's ( 1996 ) concept of dexterity is an exception to this neglect. Bernstein characterises dexterity as the ability to find solutions to novel motor problems, and he regards it as central to human motor ability. Dexterity in this sense is likely to have fairly deep evolutionary roots, being important for locomotion in arboreal primates, for example. A recent study of squirrels illuminates some of the kinds of motor problem solving that an arboreal lifestyle involves, including adjusting to the flexibility of branches, distances, and the three-dimensional configuration of space and surfaces. 4 Human dexterity shows greatly enhanced range and depth, in the sense that humans are able to solve a much wider variety of motor problems and much more complex problems (Gibson 1979 ).

Causal representation and problem solving

Mesh treats the capacity for flexible problem solving as central to human skill and proposes that it incorporates three key ingredients. Firstly, there is the ability to flexibly represent problems as causally structured wholes by means of causal models . These represent problems as structured wholes incorporating constituents and relations. At least some constituents must be represented as able to vary in state, requiring a distinction between variables and the values that variables can take. We will refer to some variables as parameters , where by this we mean key features of a type, such as an action type. The representation of causal relations requires that parameters are represented as related by production relations, such that, in the simplest case, a change in the of state of a particular parameter produces a change in the state of a second parameter.

Most accounts of action representation have focused on the representation of action form , such as motor patterns, schemas and automated procedures (Anderson, 1982 ; Buxbaum, 2001 ; Fridland, 2021 ; Pacherie & Mylopoulos, 2020 ; Schmidt, 1975 ; Wolpert et al., 1995 ). These theories propose that, when provided with a goal in a particular context, the motor system predicts what action structure will achieve the goal and then produces that structure. Crucially, there is no representation of the causal relation between the action structure and the goal, or between components of the action structure. In contrast, causal theories claim that action control incorporates explicit representation of causal relations. For instance, the individual might use awareness of the weight of an object in order to estimate how much force to use in picking it up, or awareness of the mechanical properties of a knife blade to control its manipulation when using it as a prying lever. Thus, Goldenberg ( 2013 ) proposes that action control employs a mechanical problem-solving system that represents objects and the body in terms of parts and properties relevant to action problems. Pavese ( 2021 ) argues that these representations of causal principles are practical concepts, or concepts used for intentional control of action. Somewhat similarly to Goldenberg, we suggest that causal control models are employed in action control which explicitly represent causal structure involved in action and help to identify control acts that can achieve goal states (Christensen & Bicknell, 2019 ; Christensen et al., 2015 ).

It is plausible that cognitive action representations include representations of both action forms and causal structure, but causal representation is crucial for intentional control and flexible problem solving. Intentional control of action characteristically involves producing an action with particular features because an action with these features will bring about a goal. The representation of instrumental relations is based on the representation of causal relations. Flexible problem solving involves representing the causal structure of novel problems and finding a means to produce a causal intervention which will bring about a goal state. 5

We can further illustrate the role of causal control models in action control using the example of braking. A causal control model involved in the control of braking will represent key causal factors such as braking strength , grip , speed , and braking distance as distinct, interrelated components of braking. This allows the individual to formulate a wide range of braking strategies, and adopt strategies appropriate to the conditions and their goals. Some of the possible strategies include early braking , in which gentle braking is applied far from the point at which halting or a desired speed is attained, and late braking , in which a high speed is maintained until relatively close proximity to the target point and speed is rapidly reduced by means of hard braking. A much more advanced example of the use of causal representation in formulating action strategies can be seen in a video lesson by the climber Tomoa Narasaki. 6 Narasaki is one of the best boulderers in the world, and has a dramatic style which involves frequent use of leaps between climbing positions that are far apart. These moves are called 'dynos'. In this video Narasaki explains his technique for performing a particular kind of dyno. What is of most relevance here is that he gives a detailed rationale for each component of the technique that is based on a deep causal understanding of the technique. This causal representation includes principles that can be used, not just for this particular technique, but for refining other techniques and formulating new strategies.

Our notion of causal control models is related to Pacherie's (Mylopoulos & Pacherie, 2017 ; Pacherie, 2011 ) concept of executable action concepts . Pacherie ( 2011 ) illustrated the idea of executable action concepts by contrasting them with observational action concepts which may not be executable. Thus, a spectator at an ice-skating competition may acquire the concept of a triple-axel by watching it being performed, but is unlikely to be able to perform it themselves. Pacherie argues that, since possession of the observational concept doesn't guarantee the ability to perform the action, in order to possess an executable action concept the individual must already possess motor representations capable of producing the movement. Mylopoulos and Pacherie ( 2017 ) argue that executable action concepts are executable in virtue of being linked to motor schemas which are acquired through bottom-up learning processes.

A difficulty with this account, however, is that bottom-up motor learning in most cases depends on the action being first produced intentionally. Indeed, it has been a standard assumption that skill learning involves an initial phase in which the action is produced intentionally (Anderson, 1983 ; Fitts & Posner, 1967 ). There are possible exceptions in which the structure of the movement is produced incidentally as part a larger action and consolidated by bottom-up learning (Sun et al., 2001 ). Sequence learning tasks such as the serial reaction time task (SRTT) are designed to exploit this possibility as a means for studying implicit learning. In the SRTT the participant presses buttons in response to cues (Robertson, 2007 ). They are not informed that the sequence of cues/button presses contains a pattern. On subsequent tests participants are faster at the task, indicating they have some learning of the sequence. It was hoped that tasks like this would reveal purely implicit learning, operationally measured as speeded response combined with lack of explicit awareness of the sequence. However, participants do learn some of the sequence structure explicitly while performing the task and this appears to fully explain speed improvements (Krakauer et al., 2019 ). Thus, even in tasks specifically designed to elicit implicit, bottom-up learning it has proven difficult to do so. Masters and colleagues have attempted to develop training methods which allow the movement patterns of a skill to be learned largely or entirely implicitly (Masters, 2000; Poolton et al., 2006). However, it has proved difficult to apply these methods to complex skills (e.g. Poolton & Zachry, 2007). For most complex real-world skills like performing a dance step or changing gear in a manual car there is no other practical way to initially generate the action than by intentional control.

Thus, for the most part, the individual must already be able to intentionally produce the action before bottom-up motor learning can start to occur. Bottom-up motor learning refines and consolidates movement patterns that are intentionally produced. It doesn't construct novel movement patterns. Pacherie is right that to develop an executable action concept the individual must already possess motor representations capable of producing (at least an approximation of) the reference movement pattern. But in the initial stages of motor skill acquisition the individual does not have an integrated motor representation that is specific to the movement pattern being learned. The individual usually needs to construct a cognitive representation of the desired movement as an integrated structure assembled from intentionally controllable motor components.

This brings us to a crucial phenomenon that any theory of action and skill must accommodate, namely the ability to intentionally produce novel movement patterns. The basic level of control in intentional action is not the ability to produce 'basic actions', in the philosophical sense, it is the ability to intentionally control movement parameters such as postural parameters, direction, distance, speed, force, and so on. To intentionally produce novel movement patterns as functionally integrated wholes it is necessary to represent causal interdependencies amongst at least some of these parameters, such as between position, distance, speed, time, and force. 7 Thus, causal control models of the same kind as we described for braking are used in the fundamental control of movement.

Infants and young children learn a repertoire of basic coordinated actions, including pointing, reaching, grasping, manipulating, stepping, and so on. These actions are basic in the sense that they come to function as units which will be employed in the construction of more complex actions. They incorporate stereotyped movement patterns and their control is likely to incorporate linked conceptual and motor schema representations in the way that the Mylopoulos and Pacherie model describes. But they are intentionally controllable , in the sense that their parameters can be intentionally adjusted to achieve a variety of goals. To explain this we need to supplement the Mylopoulos and Pacherie model of executable action concepts with the account of causal control models that we are proposing. Typically, in skill acquisition, such as when learning to play a musical instrument, more basic intentionally controlled movement capacities are adapted for the specific demands of the skill. During skill learning cognition leads in the construction of new actions to suit the task demands. Once a novel action structure has been constructed, consolidation and refinement will occur across the whole control system, including the formation of integrated motor schemas and the formation of integrated causal control models.

Translation between representational systems

Theories of action control face the problem of understanding translation between and within representational systems during action control, including multiple perceptual modalities, visual and verbal linguistic representations, emotion experience, gestures, computer and web interface 'languages', the iconography and signaling conventions of driving on roads, maps, music representational systems such as notation and tab, and so on. 8 Hierarchical models of intentional control, such as that of Mylopoulos and Pacherie ( 2018 ), must explain translation across different levels of abstraction. Translation across all these representational systems and levels plays a central role in problem solving and flexibility. Abstract goals and plans must be interpreted in more concrete situational terms. Flexibility hinges on being able to vary the way actions are performed in relation to features of the situation while realising the features of action critical to achieving the goals. Learning involves abstracting action features from instances in a way that allows varied concrete implementations. Recent philosophical theories of the architecture of action control, such as the DPM and Mesh models, have not so far addressed these issues but there is a long tradition of work on them in other fields (Fitch & Martins, 2014; Lashley, 1951; MacKay, 1982; Ondobaka & Bekkering, 2012). 9

A connected issue that has received recent attention in philosophy is that of the perspective of the representations involved in action control. Pavese ( 2019 ) develops the idea that some representations have a distinctive practical mode of presentation or practical perspective . She argues that these are imperative representations which specify a method of performing a task in terms of the abilities of a system that can implement the method. She claims that motor commands and motor schemas are examples of this kind of representation. We agree this is an important form of practical perspective, but we need to also understand the form of practical perspective of the representations used in the problem solving by which schemas and motor commands are formulated and evaluated. We'll call this the perspective of control . The perspective of control encompasses all of the phases, levels and aspects of control, many of which have their own perspectival characters, including those of distal decision-making and proximal control of execution.

The structure of control

In addition to causal control models, previous explications of Mesh have identified two further components of action control: forms of higher order performance and metacognitive awareness. Before describing these in detail, however, it will help to clarify the structure of control. Mesh is similar to the DPM model of Mylopoulos and Pacherie ( 2018 ) in depicting action control as involving a hierarchical structure. Mesh has not yet been very specific about the details of the nature of the control involved in the hierarchy, whereas the DPM model, based on Bratman's account of intentions (along with Searle ( 1983 ), Brand ( 1984 ), and Mele ( 1992 )), specifies a control organisation that involves multiple levels of intentions which are responsible for specific aspects of action control. In particular, a distal intention, commonly formed outside the action context, represents the overarching goal of the action. Proximal intentions are formed which specify how the distal intention is to be implemented in the context of performance. Motor intentions specify the motoric means by which proximal intentions are implemented. Here we extend Mesh by specifying the structure of control in more detail. This account shares with the DPM model the idea that intentional action often involves a hierachical goal structure, but departs from it in certain respects which in part stem from a departure from Bratman's model of intentions. 10

One way to conceptualise control is as the ability of the agent to achieve its goals. We'll call this the goal-based conception of control. Mylopoulos and Pacherie ( 2018 ) and Shepherd ( 2021 ) employ this conception. 11 A different way to conceptualise control is as the ability of an agent to solve the problems that it faces. We'll call this the problem-based conception of control. Both concepts of control are useful but the problem-based concept is important for understanding adaptive control systems and the structure of control in skill learning. Thus, when we perform a full analysis of a biological control system we need to determine both the proximal goals (the represented goals) and the ultimate goals, which are solutions to adaptive problems faced by the biological agent. These problems are to a significant degree independent of and prior to the explicit goals of the control system. Solving them is often obligatory or very difficult to avoid. The relationship between proximal goals and adaptive problems will often be imperfect, and evolution will generally tend to bring the represented goals of organisms into alignment with their adaptive problems. Proximal criteria used in the control of eating include satisfying hunger and enjoyment of the experiences of eating. The primary adaptive goal is nutrition. Humans can adopt conceptualised nutrition as an explicit goal of eating but they need not. The proximal control criteria for eating can be satisfied while the adaptive problem is not. In cases where conceptualised nutrition is a goal of eating it may correspond imperfectly to objective nutrition. Thus, goal-based and problem-based control can be dissociated.

Humans are a highly social species and are exceptionally flexible in developing varied lifestyles and technologies which have allowed the colonisation of almost every kind of terrestrial environment on earth. This flexibility in lifestyles is founded on an exceptional capacity for flexible skill learning. Human evolution has thus endowed us with skill learning capacities which are extremely good at solving the ecological and social problems we face. Uncertainty plays a central role in this flexibility. Humans face a fundamental and pervasive uncertainty concerning their goals. Their goals correspond imperfectly to their problems and they must learn about the structure of the problems that they have. Problem discovery thus plays a central role in skill learning. Skill learners typically begin with poor representations of their problems. Their goals correspond imperfectly to their problems and they must learn about the structure of the problems that they have, and learn to form better goals. This learning occurs at every level, from the specific problems involved in performing particular actions up to and including self-conception, whether to engage in the skilled activity at all, and to what degree.

More specifically, uncertainty and problem discovery play a key role in the structure of action evaluation. On a goal-based hierarchical model of control, such as the DPM model, performance at a given level of control is evaluated with respect to the goal at that level and to higher level goals. Thus, the success of motor performance is evaluated with respect to whether it achieves the goal specified by the M-intention, and whether this satisfies the goal specified by the P-intention. However, there are certain phenomena which arise quite commonly during skill learning which don't fit this model very well. An action can go according to plan yet be assessed negatively. For example, an inexperienced guitarist might perform with a band at a gig in a way that they have planned to, and which they consider to be aligned with their norms for playing well, yet later evaluate their performance negatively when they review a recording. This later evaluation may be based on performance norms they had not previously considered, but which are highlighted when they assess their performance from the perspective of a listener and compare it to performances of more advanced players they admire. For example, they might realise that their playing was overly busy, failing to complement the song, and too loud, overshadowing the rest of the band. 12

An action can also go contrary to plan yet be assessed positively; a mistake which proves to be a 'happy accident'. For instance, you might accidentally shake out more hotsauce on your eggs than intended, yet regard the outcome as superior to the intended quantity of hotsauce.

These possibilities can't be explained if the only evaluative criteria are the goals specified by the intentions.

The action evaluation system

To understand these phenomena we need to recognise a broader set of evaluative criteria. While it is often the case that a specific explicit goal operates as a primary focus of action selection and regulation processes, this goal is only one item amongst a complex set of criteria used to evaluate the action. Some criteria are low level and generalised. Thus, all actions are evaluated for efficiency, regardless of whether efficiency is an explicit constituent of the content of the goal of the action. Other criteria are higher level and also generalised. A bluegrass musician will evaluate their playing according to their internalised aesthetic norms for bluegrass music. Some norms are specific to the action type, such as technique criteria. Some norms are specific to an individual, such as a personal playing style. In the performance of any given action an ensemble of criteria will be operative in evaluation processes. Criteria other than the primary goal can be used to evaluate the primary goal and its implementation. These additional criteria are themselves imperfect and subject to learning. A novice has evaluative criteria for the skill which are impoverished and poorly reflect the norms of the skill domain. Experts often have very rich, articulated evaluative criteria. For this reason, instruction and other forms of social feedback can play a vital role in guiding learners. Techniques for self-assessment which use an external perspective, such as recording and analysing performances, are also very valuable because they allow the individual to better apply performance norms they have acquired from an observer's perspective to their own performances.

Thus, we add to Mesh the proposal that skilled action evaluation is based on an action evaluation system (AES) which develops during skill learning. 13 The AES plays a role in the cognitive processes of intention formation and in the control of action execution. Action evaluation is holistic: no single criterion has strict dominance (e.g., there is no strict master goal) and the weighting of criteria can vary across contexts. The breadth and depth of evaluation will vary across contexts, but a complex set of criteria are often operative in the control of action execution. 14

In this respect the account departs from Bratman's model of intentions. As described above, Bratman's model has a strongly punctate structure in which there is a phase of deliberation which culminates in commitment to an intention, followed by a phase in which cognitive processes are focused on implementation of the intention and evaluation of it is bracketed. Bratman's rationale for the bracketing of intention evaluation is based on cognitive resource limitations: he claims that it is not possible to continuously determine the best course of action at each point in time. But while it is true that it isn't possible to perform a comprehensive analysis of the best course of action at each point in time, this doesn't imply that intention evaluation must have the punctate structure of his model. Bratman recognises that intentions may be reconsidered when stakes are high and 'new information comes in', but he regards this as exceptional. He says that it is reasonable to have a default presumption in favour of plan stability rather than reconsideration. Concerning the circumstances in which reconsideration is reasonable, he writes:

Sometimes the stakes are quite high, and there is an opportunity for calm and careful reconsideration of one's prior plan. It seems plausible to suppose that it is in the long-run interests of an agent occasionally to reconsider what he is up to, given such opportunities for reflection and given that the stakes are high, as long as the resources used in the process of such reconsideration are themselves modest ( 1987 , p. 67).

Our model is very different. It is common to have highly imperfect information and it is hence adaptive to continue to evaluate intentions after they have been adopted, including during action execution. This allows them to be flexibly modified and abandoned as more information is gained and as circumstances change. Here we need to distinguish between evaluation of implementation intentions involved in carrying out a plan and evaluation of the overarching intention. We claim that evaluation occurs at every level. The breadth and depth of evaluation varies, and it is certainly true that there is greater opportunity for deep and wide evaluation before and after performance compared with during, but nevertheless, higher levels of control can be 'in play' during performance. Thus a professional bike rider might, during a race, re-evaluate their ability, re-evaluate their strategy for a particular obstacle, re-evaluate their race strategy, or pacing plan (Christensen & Bicknell, 2019 ; Sutton & Bicknell, 2020 ). More broadly, an athlete may re-evaluate their strategy for the season, and might even re-evaluate their commitment to racing at this level. For instance, an older rider near the end of their career might switch during a race from assessing themselves as still being competitive at the highest level to no longer being competitive, and decide on this basis to retire. At the other end of the skill career time line, we can expect that evaluation of commitment to the skill to commonly occur during performance during early phases of acquisition and at key career stages.

The basis for such evaluation is the individual's AES. A well-developed, adaptive AES represents relevant evaluative criteria at various stages of intention formation and action performance.

This model of control differs from the DPM model. In keeping with Bratman's model, on the DPM model distal intentions are ascribed the function of terminating practical reasoning about what to do. Evaluation of success is goal-based and top-down. In contrast, our model places more weight on bottom-up processes in which higher-level goals are revised in response to information gained during action execution. Evaluation of intentions does not necessarily terminate with their adoption. Evaluation of success is not only with respect to achieving the goals specified by intentions. We think the DPM model can be readily amended to accommodate the phenomena we're describing, but these are nonetheless features of control that have high significance because they play important roles in learning and flexibility.

Performance and metacognitive awareness

Performance and metacognitive forms of awareness play a key role in action evaluation. In contrast, on Bratman's account reconsideration is based on habits and dispositions, deliberation, or by policies. He thus fails to recognise the importance of such forms of awareness. Confidence, for example, can have performance and metacognitive forms. Performance confidence is awareness of the likelihood of action success. Metacognitive confidence is awareness of the extent to which sufficient information is available for effective decision-making and control. When both kinds of confidence are high, as they are likely to be when a philosophy professor makes a plan to go to the library to borrow a book, depth of evaluation can be low during decision making and performance. When these forms of confidence are much lower, as they will be when learning a new mountain bike riding technique, depth of evaluation will tend to be higher in all stages of action.

According to Mesh, causal representation contributes to performance and metacognitive awareness. A causal control model represents the causal features of the situation relevant to action decisions and control. In the case of braking this will include causal features such as the nature of the surface, the amount of grip, and the amount of braking force that can be applied. Thus, the causal control model is the basis for awareness of the performance envelope , or range of performance states that are available. In this case, awareness of the performance envelope includes awareness of the range of braking forces that can be applied without losing grip. Awareness of the performance envelope serves as a basis for evaluating whether to continue with an action during performance. If a breakdown is likely it may be best to abandon the action. At a more finegrained level, awareness of the performance envelope allows the formulation and modification of action strategies. If grip proves to be unexpectedly high, for instance when using a new type of tire, braking strategies can be modified accordingly. When the individual is uncertain they may adopt a conservative strategy and attempt to gather more information (Christensen & Bicknell, 2019 ). Thus, if they are unsure of how powerful their brakes are, for example if they are on a new or recently serviced bike, they might use early braking or ride at a slower speed as they assess the performance of the brakes. If the individual is confident they may operate closer to the edges of the envelope.

Social and cultural learning and the degrees of freedom problem

The standard approach to skill is individualistic. When seeking to understand advanced skills the focus is on the autonomous abilities of individual experts. When seeking to understand skill learning the focus is on the processes by which an individual's abilities are transformed from novice to expert. Of course, it is understood that teaching and other forms of social learning play a role in skill learning. It's understood that some skills, such as theatre, dance, music, and team sports, involve collective action. Indeed, there is burgeoning transdisciplinary interest in collaborative experiences of, and influences on, skilled performance (Bicknell & Sutton, 2022 ). Nevertheless, skill theories treat social phenomena as secondary, or subtopics of skill. Mesh has followed this individualistic orientation (but see Christensen & Sutton, 2018 ).

Attention to the larger evolutionary and ecological context indicates that we should see individual and social aspects of skill as fundamentally interwoven. Human skills are exceptionally flexible, complex, and are generally acquired and practiced in highly social ways. These associations are not accidental. The flexibility of human skill is founded on a sensorimotor system capable of many 'degrees of freedom', being able to adopt an extremely large number of configurations that can be structured in many ways over time. The diversity and complexity of human skills, in comparison with other species, is possible only because of this underlying potential. But the high dimensionality of the human sensorimotor system, combined with the complexity of many skills, presents difficult problems for learning and control. The learner confronts an extremely large problem space in which solutions must be found. The degrees of freedom of the sensorimotor system must be steered in ways that realise solutions (Bernstein, 1996 ). Learning thus presents extremely difficult search and control problems.

One way that learning is made tractable is to acquire skill progressively, beginning with basic abilities that present relatively simple problems and moving to progressively more complex abilities that build on the simpler abilities (Bryan & Harter, 1899 ). Another way that learning is made tractable is by making it social. Experts and peers provide models of high quality solutions. Teachers can guide learners through the extended pathways to complex solutions. A skill community is able to explore the space of possibilities and solutions far more effectively than a lone individual. When a member of a community discovers a superior solution or other form of valuable skill knowledge, this can be propagated through the community (Goodwin, 1994 ). Many discoveries can be combined into complex, sophisticated methods.

Rival expectations

Based on the preceding discussion we can distinguish between two main contrasting sets of expectations for the situation we are investigating. Based on the classical view we should expect major changes to technique and equipment to be highly disruptive for two reasons. Firstly, automated forms of flexibility should be unable to cope with these kinds of changes because they require qualitatively new sensorimotor patterns. Secondly, cognitive processes should also struggle to cope because they employ general purpose representations and problem solving methods. They are hence poorly suited to skilled action control, and should be overwhelmed by the alterations to the complex relations involved in action production. Mesh yields a different set of expectations. An individual with a significant amount of experience will have developed mechanisms for the cognitive control of action which allow relatively fluent coping based on problem solving. This problem solving will be based on causal representation and an action evaluation system employing a complex set of criteria. Control will be more flexible than depicted by Bratman's model, with continuous evaluation even at higher intentional levels. This problem solving will be imperfect, however, especially in an individual whose technical abilities are fairly basic. Social guidance from an expert can supplement individual problem solving by directing the learner to better solutions.

The nature of our study

We conducted a field study investigating responses to major changes in a rider with several years mountain biking experience but beginner-level technique.

We employed a researcher-practitioner approach, in which the authors served the dual roles of investigators and participants (see Bicknell, 2021 ; Downey, 2022 ; Downey et al., 2015 ; McIlwain & Sutton, 2014 , 2015 ; Nemani & Thorpe 2016 ; Olive et al. 2016 ; Ravn, 2022 ; Samudra, 2008 ; Spinney, 2006 ; Sutton & Bicknell, 2020 ). This approach brings attention to theoretically and ecologically significant aspects of skilled action in contexts that are difficult to capture in the laboratory, from the armchair, or when the researcher is unfamiliar with the nuances of a particular community of practice. Our experiences were undoubtedly shaped by our theoretical interests, and the evidence should be viewed as exploratory and tentative. Validation of the kinds of phenomena we describe is needed using other methods. These include broader ecological sampling and laboratory investigation. It is especially important that ecological methods be employed which use structured data gathering in close temporal proximity to performance with theoretically naïve participants. But it must be emphasized that all methods have strengths and limitations. The best overall research strategy is to use a large methodological toolkit and seek to buttress each investigative mode with convergent evidence from others.

The two authors of this paper have differing levels of mountain bike expertise. Kath has been a mountain bike rider for over twenty years. She has worked for global cycling media for more than ten years, taught skills clinics, raced domestically and internationally, and has written academic papers and a PhD on the sport. Wayne, in contrast, has very limited mountain bike riding experience. He is not a raw novice; he has extensive experience of recreational and commuting riding on roads and about fifteen years previously he spent about a year mountain biking regularly, riding once or twice a week. This experience included twisty 'singletrails', tracks roughly the width of a foot trail, with rutting and differences in grip due to the type of dirt underneath his wheels (eg. loose and skatey or smoother ‘hardpack’). He had largely avoided more 'technical' trails including features like ‘rock gardens’ and logs. With respect to mountain bike riding he was self-taught and as a result had not acquired some important basic mountain bike riding techniques. As part of research for a previous paper (Christensen et al., 2015 ), he read a mountain bike instruction book and gained some familiarity with basic mountain bike techniques this way. Kath had also given him some instruction during the study. He had not, however, spent a significant amount of time practicing these techniques or received any further ‘live’ riding instruction.

The fieldwork session was designed to explore and document Wayne’s experience of the trails through an initial ride with no input from Kath, followed by two major changes. After observing Wayne ride an initial series of beginner-intermediate singletrails, Kath provided instruction on core mountain bike riding techniques, which Wayne then attempted to employ. Secondly, after riding the track on his own bike, Wayne then switched to Kath’s bike, which was a more modern and capable design.

In more detail, at the time of the study Wayne’s bike was approximately ten years old, an aluminium ‘cross country’-style hardtail (meaning no rear suspension), with 26″ wheels, hydraulic disc brakes, and basic front suspension in need of a service. Kath’s ‘trail’-style bike was a few months old. The design reflected substantial changes in bike technology, trends and manufacturing materials. These included: larger 29″ wheels with wide 2.4″ tubeless tyres, which roll over obstacles with more ease and traction compared to Wayne’s smaller wheels with narrower tyres; a more stable and relaxed geometry, which adds traction and confidence on climbs and descents; far more sophisticated front and rear suspension for better traction and compliance, and other modern features such as a 1 × 12 drive train (so no shifting is needed with the left hand) and a dropper seat post which allows the rider to press a lever on the handlebars and move the seat downwards so it doesn’t catch on their thighs when they move their body toward the rear of the bike while descending. The brakes were far more powerful, the frame material (carbon) more compliant, and the handlebars substantially wider, again providing enhanced stability and control. Kath and Wayne are a similar height, meaning they both ‘fit’ the same size frame. However, when Wayne switched to Kath’s bike the contact points were adjusted for Wayne, with the seat height being modified and his own pedals used.

The trail used for this investigation, a popular cross-country loop at the Ourimbah trail network in New South Wales, Australia, was chosen on the basis that Wayne could tackle it with reasonable safety, with guidance from Kath, but which contained obstacles that were more challenging than Wayne's prior experience made him comfortable with. During the ride, Kath gave Wayne the kind of instruction and induction into the mountain bike subculture that would be characteristic of an experienced rider taking a friend on a ride for the first or second time. For example, when Wayne was concerned about riding over a log obstacle she explained and demonstrated key body movements and the amount of speed needed to do it safely, but also encouraged him to walk the obstacle and try it again later if he preferred.

We took photos and recorded video of Wayne’s successive attempts at challenging obstacles on both bikes, and recorded video of Wayne describing his immediate responses to these experiences. We met the next day to write notes on the experience.

Riding the Ourimbah cross-country track

There were two aspects that stood out as especially noteworthy in Wayne’s experience: a change in technique that dramatically improved descending steeper trails, and adaptations to the increased performance capabilities provided by the second bike, particularly due to its stability over rough terrain.

At the beginning of our fieldwork session, Wayne rode a short loop of ‘singletrails’ without instruction or interference from Kath, who rode behind him, watching and observing. These trails included several relatively steep descending sections of trail, linked together by narrow, winding, rocky connections and the occasional smooth section for the rider to relax and catch their breath. (Understandably) Wayne was riding somewhat nervously and cautiously. He felt that his approach to riding these trails was reasonable given his limited overall experience and that he had not ridden a mountain bike trail in a number of years. Riding behind him, however, Kath could identify specific technical problems. She could see and hear the rear wheel skidding and noticed that Wayne’s body position (and centre of gravity) was quite far forward on the bike. This puts a lot of pressure on the front wheel, which causes several problems while descending, the most severe of which is that it increases the risk of flipping over the front of the bike. In the mountain biking subculture this is referred to as an ‘OTB’ (over the bars)—the standardization of the term suggesting it is a fairly common experience but one to be wary of.

We paused on a long flat section of fire road to discuss the experience of the first section of trails. Worried that Wayne was going to injure himself, Kath provided instructions that would help him ride the section more smoothly and safely on the next attempt. She explained a technique for descending steep obstacles, which involves keeping the feet balanced evenly on the pedals while the rider moves their bodyweight rearward on the bike to maintain balance and stability. She explained this to Wayne verbally, along with an arm gesture showing the effect of weight on the bike in relation to the terrain. She used the cue words ‘butt back’ and ‘weight back’ as a shorthand way to emphasize and direct this technique after the initial description of what to do. Taking advantage of the less threatening and challenging terrain provided by the wide, flat fire road, Kath showed Wayne an exercise which encouraged him to experiment with how far he could move his body rearwards while maintaining momentum on the bike. This involved riding in a straight line at a moderate speed, moving his butt behind the saddle and allowing it to graze the rear tyre. This exercise was designed to increase his awareness of how much space he had to move rearward on the bike, what this felt like in practice, and how this sensation differed to what Wayne thought was the near-maximum amount he could get his bodyweight behind the bike. 15 The sound and feel of grazing the tire provide aural and kinaesthetic cues that signify the rider has succeeded in the task when it is not possible to see, visually, how far back they have reached.

We then rode the same short sequence of trails again. Wayne now focused on getting behind the saddle during any steep downward sections of trail. The result was a dramatic improvement in the controllability of the bike. This transformed the experience of sustained descents and short, sharp drops or ‘rolldowns’. Rather than feeling threatening, the experience of riding these obstacles felt relatively comfortable—it felt less steep, less rough, less like he was about to have a crash. After applying the technique cautiously to a rocky ‘stepdown’, Wayne immediately began to apply the same technique to other steep, downward sections of the trails: log rolls, steep downward corners, rocky rolldowns, anything where the front of the bike is much lower than the rear of the bike while riding a trail obstacle.

After a longer lap of the trail on Wayne’s bike, Kath gave him her own bike to use for the second lap. She had another bike waiting in the car to facilitate this exercise. The technological differences described above between Kath’s bike and Wayne’s older model bike indicate that Kath’s bike would be more stable, compliant, absorbent and confidence inspiring. While this makes sense on paper, Wayne was nevertheless astonished at how much more capable the bike was in practice. The most immediately striking feature of the bike to him was its greater stability, and the fact that this allowed much better low speed control than his own bike. In comparison, the relative instability of Wayne’s own bike meant it would often feel like it was going to tip over at slow speeds.

The combination of greatly enhanced stability and compliance had a dramatic effect on Wayne’s experience riding the trail. He was able to ride faster on bumpier sections, with the bike soaking up rocks and gaps between them that would produce strong jarring on his own bike. Within 10–20 min of riding this bike his confidence greatly increased. Many descents and ascents he had struggled on while riding a lap of the trails on his own bike—rooty straights, rocky windy uphills, small rocky step-ups, uphill corners, rutted entries into corners, descents littered with a messy array of small obstacles—were experienced as being much more ‘ridable’ than they had been earlier that same day. The bike maintained traction more easily allowing Wayne to pedal and guide it through the obstacle with more control, confidence and ease. He continued to approach some obstacles cautiously, however, and Kath spent some time teaching him to ride over a log, which looked difficult to him but in fact was not. His main difficulty was simply overcoming his fear of the obstacle so that his approach was fast enough to carry him over the rocky ramp that led to the log itself. We make no claims that Wayne was able to ride all sections of the trail, or ride flawlessly ever after. He subsequently crashed when he misjudged a rocky section later in the day, bruising his ankle and wrist and acquiring some grazing.

Finding theory in action

In this section we use the theoretical concepts introduced in Sect.  2.2 to characterise the processes by which Wayne adjusted to the new technique and the new bike. Wayne was familiar with these concepts, which makes it easy for him to describe his riding experiences in these terms. This familiarity also undoubtedly influenced Wayne's experiences during our field study. However, based on Kath’s extensive experience teaching skills clinics, and riding with mountain bikers at a diverse range of skillsets, we think that the kinds of problem solving Wayne engaged in are not unusual. In particular, his experiences of identifying control problems and experimenting with solutions were in many respects fairly typical for a beginner rider.

Simple forms of adaptation

Wayne adapted rapidly to the new technique and new bike. Some of this adaptation involved relatively simple forms of flexibility. Simple calibration changes in important parameters, such as braking forces and braking timing, played a role. Both of the major changes improved stability, which had the effect of making feedback control more tolerant or less 'twitchy'. Feedback control processes quickly recalibrated for the new tolerances. As riding became more manageable Wayne could relax more, and reduced bodily stiffness is likely to have made control easier; bumps and other perturbations which might have been jarring and disruptive would now be absorbed more effectively through his limbs. Experience selectively highlighted key parameters, allowing control to be more focused.

Problem solving using causal knowledge and metacognition

Nevertheless, the changes in technique and bike were large enough to require adjustments by means of strategies formulated using problem solving. Wayne's ability to adapt to large changes hinged critically on an ability to formulate and implement new action strategies 'on the fly'. We can illustrate this by describing in detail a change in riding strategy associated with the technique change of getting behind the saddle. Although Kath wasn’t aware of it, the rear wheel skidding that she observed during Wayne’s initial ride was partly the result of a deliberate braking strategy that he thinks of as ‘tail dragging’, which involves using primarily the rear brake. After adopting the behind-the-saddle technique Wayne switched to an equalized front-rear braking strategy. Wayne was familiar with rear-wheel-based braking from riding as a child, with this experience including riding ‘back pedal’ brake bikes and with using the rear brake to skid out the back wheel on loose surfaces when coming to a halt, a satisfying and popular maneuver. He knew, however, that equalized braking is regarded as the superior technique and he had generally used it in his previous mountain bike riding. He nevertheless initially used rear-biased braking as an improvised strategy in response to control problems that he was experiencing. Wayne wasn’t aware that his weight was too far forward, but he was aware of some of the consequences of this. The load on the front wheel caused instability and had the potential to cause the front wheel to lose grip and slide out, resulting in a crash (which Wayne thought of as a ‘washout’). 16 Wayne felt that the wheel was most likely to lose grip under braking and using a rear-biased braking strategy helped to reduce this risk.

Thus, causal awareness played a key role in the adoption of the tail-dragging strategy and the later switch to equalized braking. Here we should note that Wayne's perception of the most immediate and important risk that he faced differed from Kath's assessment. Wayne was preoccupied with the danger of a washout due to heavy front braking, whereas Kath viewed the primary risk in Wayne's riding during this phase as being a front wheel washout or OTB crash caused by insufficient rearward weight. Other riders in a similar situation might have interpreted their risks differently and adopted different strategies. Other strategies which reduce the risk of loss of control when riding a difficult descent include putting one or both feet on the ground and scooting down, using both brakes fairly heavily and ‘inching’ down the obstacle (generally ill-advised), avoiding braking all together and focusing on body position, balance and looking ahead to the exit of the obstacle, or getting off the bike and walking (or sliding) the bike down the obstacle.

Metacognition can also be seen in this example. While Wayne thought there was a danger of the front wheel sliding out, he didn’t know in detail in what conditions this could occur. He was still adjusting to the ‘feel’ of the bike on the terrain and was uncertain about the amount of grip available and the braking forces that could be used. That is to say, Wayne was aware that he lacked sufficient information for good control. Estimating these action parameters is complicated by the fact that they are strongly affected by the nature of the surface, which was variable, and by the fact that in a washout the loss of grip tends to be abrupt. In the face of this uncertainty, tail dragging combined with low speed was a relatively safe, conservative strategy. And it worked! Wayne did manage to ride these difficult sections of trail without crashing. Kath’s intervention was to help Wayne ride them more smoothly, more safely and, ultimately, more enjoyably.

Metacognition also influenced Wayne’s use of tail dragging in another way. Tail dragging is a simple strategy to employ because there is no need to precisely coordinate front and rear braking pressures. Wayne was experiencing high cognitive load because he needed to pick a line with care over the deep rutting of the trail to ensure that the front wheel did not glance off the side of a rut and get channeled down it, resulting in a crash. In addition, Wayne was experiencing significant jarring through the handlebars, and he was concerned that if he hit a bump while braking he might accidentally grab the front brake too hard. Tail dragging simplified the cognitive demands of braking and allowed him to direct more attention to line choice. That is, the choice of strategy was based in part on awareness of excessive cognitive load and the need to reduce this load.

One of the main problems with tail dragging is that it reduces effective braking power because braking force is provided by only one wheel, and because it often results in the rear wheel skidding. This in turn means that speed must be kept low. Partly for this reason Wayne maintained a fairly low speed during the descents, but he preferred to ride at a relatively low speed in any case to allow more time for line choice and to minimize the consequences of a crash. He thus didn’t regard the speed limitations of tail dragging as a reason to avoid it in this context.

However, after Wayne began getting fully behind the seat while descending he switched to equalized braking. This was because the control problems that prompted the tail dragging strategy had been largely eliminated. Independently of any detailed causal understanding, the rearward riding posture leads to several changes in the feel and handling of the bike which provide greater sense of control on steep sections of trail. Cues indicating instability are reduced and handling is improved. The arms are more extended, which reduces unwanted side-to-side rotation of the handlebars and, consequently, the front wheel (compared to the freedom of movement that comes with a larger bend at the elbows). But Wayne was more specifically aware that with his weight now towards the rear there was a greatly reduced risk that the front wheel would lock up under braking. The risk of an over-the-bars crash was also much lower. As noted, this danger had not been at the forefront of Wayne's mind but he was aware of it (he had experienced such a crash previously). Now that his weight was positioned rearwards, and the bike could rotate forwards without pitching him forwards, he became aware that an OTB crash was a lot less likely.

Indeed, the front wheel could now rise and fall much more easily as it tracked over obstacles. This made line choice less critical because there was less chance that the front wheel would glance sideways when it struck the side of a rut. This reduced cognitive load. There was less jarring through the handlebars, making it easier to judge and execute braking pressures. With braking distributed between front and rear, overall grip was increased and there was less chance of either wheel skidding. Wayne became more confident about applying much stronger braking pressures than he had previously. And since the improved handling made similar riding problems more tractable, he became more generally confident about tackling various kinds of descent obstacles. There were distinct limits to these improvements, however, and there were some descents that he still regarded as too challenging. For these he would dismount and walk.

We can illustrate changes in strategy in response to the new bike with the example of a decision to tackle a particular ascent. It was short, relatively steep and had a somewhat loose surface. Wayne tackled it several times on his own bike and once on Kath’s. On his own bike Wayne found the ascent challenging because he needed to begin with high momentum in order to climb it. There were two problems that contributed to this. One was that he had relatively little grip because of the geometry of his bike and the tires. Specifically, on this slope, with its loose surface, if his speed became too slow while using high power pedal strokes the rear wheel could lose traction and 'spin out'. The other was that his riding position on this bike had a relatively high and forward center of gravity, which meant that the bike felt unstable and 'tippy' when riding at slow speeds. If Wayne was going too slow he needed to come to a complete halt and dismount, or he would fall over. The approach to the ascent was downhill, and each time he made the approach he needed to quickly decide whether he had the right line and was going fast enough to make the ascent successfully. He made it up the first time on his bike but stopped on the second attempt because he didn’t think he was going fast enough.

On Kath’s bike Wayne decided to tackle the ascent even though his approach was slow. This point is worth emphasizing because it highlights the way that the different capacities of the new bike led him to use altered riding strategies for obstacles that he had not yet experienced on the bike. Had Wayne been on his own bike he would not have attempted the ascent with the approach that he had at this point. He did attempt the climb because he was confident that the low speed stability of the bike and its grip would allow him to ride it slowly, with less risk of falling over and less risk of losing traction. This proved to be the case. He found that he could come to a near halt during the climb without falling over, and the increased grip of the tires meant that he could use slow, high power pedal strokes without the rear wheel spinning out.

To sum up, Wayne was able to construct riding strategies ‘on the fly’ based on causal and metacognitive awareness. He could form, evaluate and modify strategies based on awareness of factors such as instability and threatened loss of grip. The strategies could take into account multiple factors, reflecting an awareness of how causal factors interrelate in riding. Wayne also selected and adjusted strategies based on sensed uncertainty and risk. This problem solving ability extended to large changes in causal relations associated with major changes in technique and equipment, and hence allowed him to cope with these changes.

It's important to note that Wayne's adaptations went beyond the formulation of specific strategies for particular problems. Wayne showed generalised learning in the sense that each major change allowed him to solve new classes of control problems. As he formulated and implemented new strategies he was also learning about the underlying causal structure of control. He was, thus, extending his causal control model as well as refining it.

Difficulties in adaptation

Difficulties and limitations in Wayne's adjustments are also revealing.

The new bike had only a rear derailleur rather than front and rear. This simplified changing gears but Wayne had well-entrenched gear changing methods which involved coordinated shifting of front and rear derailleurs. It's worth emphasising just how important gear changing is in mountain bike riding. With frequent, rapid changes in gradient and other trail features, it's necessary to change gears often. Smooth, fast riding depends on anticipative gear shifts, especially when the change in gear is large. When encountering a steep slope, for instance, the rider may need to shift from a high to a low gear, and be in the right gear to effectively apply power as speed slows. It's desirable to maintain as much speed and momentum as possible. Wayne's technique for such a situation involved making several shifts in sequence. An initial anticipative shift selects the middle or small front chain ring (lower range gears) and a rear gear that is medium-to-low but high enough to 'catch' the initial phase of slow-down and extend the speed and momentum. Multiple subsequent shifts downward are then made, using the rear derailleur, as slow-down continues, until the right gear for sustained climb is reached. Selecting the wrong gear for a shift disrupts the smooth progression. When the gear is too high or too low the rider will 'bog down' or spin, and either way lose speed and momentum. A further consideration on Wayne's bike was that his gears would sometimes not shift under heavy load, making it important to shift before high power output was required. This was not the case with Kath's bike, which shifted smoothly during high-power pedaling on climbs.

On Kath's bike Wayne had to inhibit his urge to operate the front gear system and reorganise the way that he made anticipative gear shifts. This required heightened attention. An especially attention-drawing feature of the alteration was that in the location where Wayne would operate his front derailleur there was a lever to activate the 'dropper' post. This, in combination with pressure (or lack of) on the saddle, lowered and raised the seat. Lowering the seat during descents gives more freedom to move backwards and forwards as needed. But having the seat drop is not something which the rider will want to happen unexpectedly when trying to change gears or pedal up a hill. When raised the seat would spring upwards to its normal position, and was, in effect, a spring-loaded piston driving towards the rider's crotch. This bike feature was unlike any that Wayne had previously experienced and he found it somewhat disconcerting.

Wayne was able to modify his gear change method and learn to use the dropper post, but these adjustments were more effortful and less smooth than those described in the previous section. Why this should be so raises interesting questions. In general, it's reasonable to expect that some modifications to control are easier to make than others because the control system is better prepared to handle some kinds of change than others. Piaget's ( 2015 ) distinction between assimilation and accommodation is one expression of this idea. In the Piagetian picture increasingly powerful/flexible forms of problem solving ability develop in a progressive sequence as more abstract/deep concepts are learned. The Einstellung (Luchins, 1942 ) and functional fixedness effects (Duncker, 1945 ) are manifestations of the somewhat contrary-seeming phenomenon of increases in rigidity with learning. There is no deep conflict, however. Learning can involve increases in rigidity with respect to some aspects of control together with increases in flexibility with respect to others.

We can develop a preliminary explanation for differences in difficulty in this case which draws on the resources developed in Sect.  2.2 . With respect to the new bike, changes in attributes such as stability and grip were relatively easy for Wayne to incorporate into his riding in at least an initial, basic way. This may be because, although the parameter values were substantially different to his own bike, the parameters themselves, and their role in control, were reasonably familiar. He could therefore adjust his existing methods relatively smoothly. But other differences involved more substantial changes in causal relations and more extensive changes in control operations. Thus, a familiar operation needed to be 'remapped' to a different mechanism with drastically different causal effects, along lines such as {[L-LEVER-OP → F-GEAR-OP] ⇒ [L-LEVER-OP → SEAT-OP]}. Since the operations involved considerable novel structure, the structure needed to be composed in working memory, with implementation and monitoring requiring greater attention than more familiar control operations.

More generally, based on the causal control model account we could expect that skill learning will often exhibit a somewhat Piagetian pattern of increases in generalisation and flexibility which arise as the learner learns to solve varied causal problems. More generalised causal representations develop which capture deeper structure, and more powerful and flexible forms of control develop in order to efficiently manage varied problems.

An even stronger limitation in Wayne's ability to solve the riding problems he was facing is evident in the fact that he needed instruction on the correct implementation of the behind-the-seat technique. During the initial ride he was aware that he was experiencing control problems and rode cautiously for this reason. But he was unable to diagnose the source of these problems to specific technical flaws. At this point he assumed that he simply needed more experience in order to improve calibration and refinement, as opposed to making large technical changes.

This failure in problem solving is all the more striking because he understood the technique abstractly and believed he was implementing it. When Kath explained verbally the technique of getting behind the saddle during descents, the information was already familiar. He had not known of the technique when he was mountain bike riding by himself many years previously, but he had since learned of it from a mountain bike instruction book. He knew that good riding technique involves shifting one’s weight backwards during a descent to maintain even weight distribution across both wheels. What he didn’t realize is that he was implementing this technique incorrectly. More specifically, he didn’t realize that he wasn’t moving nearly as far backwards as he could and should. From his perspective it seemed like he was moving backwards to about the limits of rearward movement for his body. This was far from being the case.

To understand how Wayne could be as mistaken as he was about this it will help to note that in riding on roads—which was the bulk of his riding experience—there is relatively little need for front-rear body movement. Consequently, a relatively small amount of rearwards movement felt like a lot. Moreover, although Wayne knew that it was important to maintain even weight distribution across the front and rear wheels, he was not used to maintaining this form of awareness and had been preoccupied by line choice. There is a distinctive ‘feel’ to a weight distribution that is too far forward in a descent, which notably involves pressure on the hands and wrists. Wayne had not yet learned to efficiently identify this and respond appropriately.

Thus, although Wayne was able to detect the front wheel instability and formulate a compensatory strategy, he failed to autonomously find a much more effective strategy. This stemmed from a poor representation of weight and balance and a poor awareness of his ability to adjust balance. He failed to properly relate the instability to a forward weight distribution and solve this by moving far enough rearwards. This is despite the fact that he knew the correct technique abstractly. A poor on-the-bike representation of balance contributed to a failure to properly interpret the abstract instructions.

We can interpret these points in terms of the concepts of causal control models, action evaluation systems, and problem discovery. Wayne experienced cues to poor control in his initial ride which prompted him to ride cautiously. But his ability to represent the causes of these problems was underdeveloped and so, while he found a solution that achieved the goal of riding the obstacle, he failed to find a more optimal (smoother, safer, speedier) solution. Once he had learned the superior technique his causal control model was altered and his capacity for action evaluation improved. He became aware of an expanded range of body movements and as he experimented with this range he gained new information about the interrelations between weight distribution, stability and handling. He could now interpret high pressure through the wrists as a sign of weight being too far forward. He could better interpret perceptual cues related to balance and perform bodily adjustments to modify weight distribution more appropriately. Putting this in more general terms, he had acquired a revised understanding of balance control on the bike which yielded a generalised improvement in his ability to solve riding problems.

The problem of translating between representational systems

The difficulties Wayne experienced involved a failure to properly translate between abstract and situated representational systems. As such, they help to reveal how these mappings are constructed. In the earliest stages of skill learning the individual must laboriously construct concrete interpretations of abstract action descriptions. This is hampered by two factors. Firstly, the individual lacks systematic representations of skill-specific phenomena at the level of concrete control of execution. Secondly, the individual lacks well-developed systematic mappings from abstract to concrete representations. In this case Wayne lacked a fully-developed systematic representation of the range of positions he could take on the bike and their relations to balance. Once he had learned to move his body backwards, and experienced the technique in an approximation of its correct form, he developed an awareness of balance and stability which he could relate to his abstract knowledge of the structure of the technique. He had thus developed a representation of the structure of the technique from the perspective of control which he could use for control. One way to describe this is that he had now acquired a relatively well-structured executable action concept for the technique. However, much more practice would be needed to consolidate this concept in relation to a well-developed causal control model for implementation.

Improvements in the structure of control

Wayne experienced a significant degree of uncertainty throughout the ride. His intentions, in particular the riding strategies he adopted, involved commitment that was always qualified and evaluated during performance. He maintained awareness of opportunities to abort actions and he did so on several occasions, such as the one described above where he initiated an ascent but stopped part way for fear of losing traction at a higher section of the ascent and falling over. He modified strategies both prior to and during execution to reduce problems, increase the chances of success, and, later, to exploit improved capabilities. His intentions were thus much more labile than Bratman's model recognises. This lability was based on an action evaluation system which could evaluate intentions against a complex ensemble of further criteria represented by the AES. Indeed, the learning process hinged on this.

Wayne's uncertainty was especially high in the initial stages of the ride. He was unsure of which trail sections he could and could not ride safely and he was unsure of whether his riding strategies would be effective in negotiating obstacles and avoiding crashes. His ability to evaluate his performance was also limited, as evident in his flawed diagnosis of the stability problems he was experiencing. Thus, his control ability was relatively poor in both the goal-based and problem-based senses. His ability to achieve the goals that he had was modest and not reliable enough to provide reasonable confidence. But he was also uncertain about his goals, and not able to form all of the right goals, because he lacked a good understanding of his riding problems.

Wayne’s control ability improved over the course of the ride in both the goal-based and problem-based senses. He became better able to achieve the goals that he had, and he became better able to solve the riding problems that he faced. This was based in part on improvements in his understanding of his riding problems, which included greater ability to evaluate action strategies and performance, and form appropriate goals. Improvements in evaluation ability, with enhanced ability to manage uncertainty, are critical to mountain biking, which routinely involves riding unfamiliar, challenging trails.

The technical improvements in particular involved a relatively deep form of problem discovery. As Wayne learned how to properly implement the behind-the-seat technique he was learning both technique-specific and more generalised representations. He was refining a technique-specific concept linked to a causal control model for the technique. These representations incorporated more generalised representations, such as of body position on the bike, balance state, terrain, grip, speed, and so on. These more generalised representations allowed improved representation of a larger set of riding problems. This enabled the formulation of an expanded range of riding strategies which were more effective. These representations also improved the ability to interrelate techniques and strategies by means of common features. Thus, when one strategy is succeeded by another, such as a descent followed by the negotiation of a corner, features of each can be related to each other and adjusted to provide good fit. The speed and line of the descent can be shaped to set up a good entry to the corner and an efficient cornering line, for instance. Action-specific representations are thus integrated into a global state-space of control which represents the situation, performance state, and action possibilities. This state-space can be more or less well integrated, and the more integrated it is, the better able the individual will be to produce coherent complex action.

Social and cultural influences

Wayne's prior mountain bike riding experience was not insignificant, involving about a year of riding one or more times a week on a mixed set of singletrack and fire trails. This provided him with enough skill to engage in the problem-solving described above. But his learning was based largely on solo discovery, adapting skills from road riding and casual BMX-style riding in childhood. The kind of flaws in we’ve described in Wayne's technique, and the limitations in his problem solving ability, are common in individuals who attempt to teach themselves complex skills. Rich engagement with teachers and a skill community can scaffold skill development, allowing the individual to develop solutions and an expanded sense of what is possible (Aggerholm & Hølbjerre-Larsen, 2017 ).

One of the most important ways that a skill community can guide individual learning is by furnishing skill norms. As we described above, during the initial ride Wayne was aware that he was experiencing control problems but thought his riding was reasonable given his experience and the context. This is an example of a general phenomenon: learners tend to possess impoverished norms for the skill domain, and this hampers their ability to evaluate and improve their performance. In this case, Wayne did not have a good grasp of what kind of performance he should be able to achieve, given his base skill level. In fact, at that point he could relatively easily achieve a much higher level of performance. In general, it is difficult for learners to know what performance standards could be expected for their level and experience. This in turn limits their ability to diagnose problems in their methods.

Skill communities often have highly developed performance norms which orient individuals. In mountain biking, speed is a highly valued performance norm. Wayne was not especially concerned with speed at this point, and Kath found it amusing that Wayne’s initial reactions to the new bike were primarily focused on its improved low speed handling. Speed, though, is only one element of a set of norms for assessing quality of performance. Smoothness and efficiency are also valued. These attributes are integrated into an umbrella concept of flow , 17 which serves as a goal for riders and trail builders (Bicknell, 2016 ). Other norms concern safety and risk management. In this respect, a feature of the risk norms for mountain biking that is striking to Wayne is the acceptance that there is a fairly high level of risk that is ineliminable and must simply be accepted. Crashes and injuries are simply part of mountain biking. Thus, through social interaction learners acquire concepts for normatively characterising performance which help them to evaluate their own performances and set goals. Group riding, both social and competition, exposes the individual directly to the performance abilities and conventions of others, providing further information for self-evaluation and goal setting.

The ability to use social information is itself skill-dependent. For example, in riding with Kath, Wayne was aware that she was a more capable rider. However, from this he gained little information that was useful for his own riding. Because Kath was so much more advanced, her performance ability did not serve as a useful benchmark for him and he was unable to identify the structure of her methods in a way that would allow him to copy them. Individuals with more advanced skill are often much better at identifying the structure of methods used by others (Bicknell, 2010 , 2011 ). This can allow them to copy and adapt them for themselves, or identify problems which should be avoided or corrected. Teaching can scaffold learners through this limitation. The learner's limited action evaluation system is supplemented by the much more sophisticated action evaluation system of the teacher. In such situations, the causal control model of the more experienced rider can help to develop the causal control model of the learner.

The mountain bike community routinely scaffolds the experiences of the individual in other ways, too. The difficulty and type of experience that riders have is shaped by trail and bike design. Trail grading gives riders information about the difficulty of particular trails which allows riders to decide in advance whether a trail will be within their ability. Grading systems like this also provide individuals with benchmarks for assessing performance. If an individual finds an intermediate trail difficult they are able to locate themselves within the spectrum of abilities within the community. This can in turn guide goal setting for learning and performance. An individual having difficulties at a particular level can seek to identify technical limitations that are holding them back, and work on those, for example. At a more immediate level, signage on trails alerts riders to the required skill level needed to ride specific, upcoming obstacles. Double- or triple-downward arrows before a particularly steep section of trail that isn’t clearly visible on approach serve this function on some trails, alerting the rider to challenging terrain they typically cannot see until they are already riding it. In other trail communities a sign saying ‘warning’ may be used. An alternative convention is signs pointing to A-, B- or C-lines, indicating the technical difficulty of upcoming sections of trail and encouraging riders to make a decision about which line to take (Fig. ​ (Fig.1 1 ).

Trail sign, Ourimbah cross-country mountain bike track. This sign prompts riders to prepare for the jump line on the right, or to veer left to avoid it. Importantly, it is placed on an unremarkable section of track, at eye height, to catch the rider’s attention with enough time to make a decision about how to approach the upcoming section of trail, without having to stop riding in order to do so. Photo by Kath Bicknell.

Indeed, the mountain bike community has a rich set of community-specific caretaking practices which manage awareness, decision making and problem solving. Additional practices include:

• ‘Pre-riding’ (looking at a trail slowly, with an explorative mindset, before riding it at speed);
• ‘Sessioning an obstacle’ (stopping to look at and practice an obstacle before riding it at speed);
• ‘Riding and scoping’ (eg. riding around a jump while looking at it sideways during the first run of a trail and deciding whether it is safe to attempt on the next lap of that section of trail);
• Building up ‘reserve techniques’ (which can help to regain control if the speed, shape or pitch of the bike mid-obstacle indicates a crash is imminent).

Communication methods are also used to manage problem solving. For example, the question, ‘Is it rollable?’ is one that a skilled rider may ask another before attempting a trail for the first time. If a trail is ‘rollable’ it means there are no gaps that need to be jumped—as long as balance and momentum are maintained everything is ‘rideable’. The trail may well be frighteningly steep and require a high skillset in a number of other areas, but it will be manageable for a rider with a particular set of abilities.

To sum up, skill communities scaffold learning and performance in many ways, allowing higher levels of performance to be achieved and a better, safer quality of experience. The practices which achieve this do so to a significant degree by enhancing the decision-making and problem solving of the individual. Indeed, the engaging nature of sports like mountain biking rests on achieving a complex balance between approachability, challenge, and safety. Mountain biking has been very successful in this respect, and is a fast-growing sport (eg. Latz, 2020 ). This point is worth emphasising because it helps make the case that the phenomena we’ve been describing are not marginal or unimportant—they’re integral to many skills and can be crucial to their success.

The ability to formulate action strategies and control their execution is a central issue for understanding action and skill, yet there is very little work on it. Here we found that even an individual with relatively modest skill experience can be capable of fairly complex, fast-paced construction and control of action strategies. Our results need to be validated by further ecological and laboratory-based investigation but we are confident that the core phenomena we've described are real, and that the use of strategies and problem solving is very common in skilled action. The kind of problem solving we found, together with its flaws, is likely to be fairly typical for individuals in relatively early stages of skill learning. But in skills which require significant levels of flexibility—such as mountain biking and climbing—problem solving is also likely to be central to the most advanced levels of skill. It is consequently of high importance that we develop a better understanding of the mechanisms which support these abilities and the way they develop during skill learning. We've argued that causal representation, performance awareness, metacognitive awareness and action evaluation all play important roles and operate together in a complex, integrated action control system. Our account of these mechanisms goes beyond previous work and adds to the Mesh theory of skill a more detailed model of action control.

Acknowledgements

We thank John Sutton, John Michael, Josh Shepherd, Chiara Brozzo, Cynthia Siew, Gaye Camm, Tom McClelland, Doris McIlwain, and the generosity of colleagues and students in the Cognitive Ecologies and Microethnography Labs at Macquarie University, Sydney, Australia. An earlier version of this paper was presented as a keynote at the ‘Actions: The Mental and the Bodily’ conference at the University of Warwick, UK, and in a shorter form at the ‘Cognitive Futures in the Arts and Humanities: Paradigms of Understanding—Sharing Cognitive Worlds’ conference in Mainz, Germany. We thank the audiences for their questions, provocations and enthusiasm for this work.

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research was funded by European Research Council starting Grant 757698, awarded under the Horizon 2020 program for research and innovation, and the Australian Research Council Discovery Project grants DP130100756 ‘Mindful Bodies in Action: a philosophical study of skilled movement’, awarded to Doris McIlwain and John Sutton (2013–2015) and DP180100107 ‘The Cognitive Ecologies of Collaborative Embodied Skills’, awarded to John Sutton (2018–2020).

Declarations

The authors report no conflict of interest.

1 See Preston ( 2013 ) for a related discussion of Bratman’s account.

2 See Chisholm ( 2008 ) for an in-depth exploration of the relationship between bodily characteristics, technique and environment in climbing.

3 This video shows six different strategies used to solve a particular problem at the 2021 World Championships: https://www.instagram.com/p/CT9pAfTpMmm/ .

4 A video of squirrels engaging in this kind of problem solving can be seen here: https://theconversation.com/we-used-peanuts-and-a-climbing-wall-to-learn-how-squirrels-judge-their-leaps-so-successfully-and-how-their-skills-could-inspire-more-nimble-robots-165524 .

5 Nanay (2020) and Fridland ( 2021 ) argue that action control is based on imagistic representation, drawing on evidence that practice using mental imagery can be highly beneficial. Causal control models should be differentiated from mental images. Imagistic representations do not represent causal relations per se, although causal representation can be incorporated into imagistic representations. Nor is causal representation necessarily imagistic. It is the representation of causal relations specifically that plays a foundational enabling role in the intentional control of action.

6 https://www.youtube.com/watch?v=IqsNJv2VROs .

7 Not all parameter relations need be represented as causally related since the motor system can sometimes ‘fill-in’ some parameters when given others as explicit goals. Indeed, because of the limits of attention capacity learners must often rely on some degree of ‘filling-in’ because they can’t continuously attend to the full structure of the technique for a novel action while performing it. For instance, when learning a new chord the learner might initially attend primarily to landing finger locations then switch to correcting overall hand posture, while largely ignoring timing. But these kinds of cases illustrate limitations of automated ‘filling-in’ because the filled-in parameters will often not correspond to good technique. Focusing on finger locations while neglecting hand posture when learning a new chord is likely to result in poor posture. Once an individual has developed a high level of skill automated filling-in will tend to better correspond to good technique, but this is because the techniques have already been intentionally learned. We thank a referee for prompting this clarification.

8 We’re using the term ‘translation’ in a broad sense which involves the construction of alternative representations of the same, similar or related content (see Christensen, 2020). This includes translation between alternative expressions within a representational system such as a language. An alternative approach would be to restrict ‘translation’ to content re-representations and interpretations across representational systems with distinct formats and use an alternative concept such as ‘mapping’ for such content relating processes within a representational system, such as between levels of abstraction (we thank a reviewer for this suggestion). This raises a complex set of issues. We agree that there may be reasons to adopt a more restricted concept of translation but we think it is unclear how this should be done. In general, we think that philosophers and cognitive scientists have been overly focused on the idea that mental representational systems are discrete, static systems defined by a distinct, unchanging format Evidence from cognitive neuroscience suggests that the brain has an extremely flexible ability to construct representational systems, that representational systems with different formats can overlap (auditory, visual, and haptic representational systems use shared spatial representations), that multiple formats can be encompassed in an integrated representational system (working memory is an integrated workspace which includes auditory, visual and other subcomponents), and so on (see Author 2020 for an extended discussion). Consequently, we doubt that it will be possible to draw a clear distinction between within-system and between-system content-relating processes. More specifically, it’s likely that content-relating and construction processes that operate within cognitive representational systems also play a role in constructing new representational systems and building relations across existing systems when this becomes functionally beneficial. These points have strong implications for understanding skill learning, which, we suggest, involves the construction of multiple interlinked domain-specific representational systems. In future work we’ll develop the idea that the processes we described here as translating between levels of abstraction play a key role in the formation of new representational systems for skill control.

9 Recent philosophical discussion has been preoccupied with a narrower 'interface problem' formulated by Butterfill and Sinigaglia (2014). The idea is that there is a special difficulty understanding how cognitive and motor representations can interact which stems from the supposed impossibility of translation between their formats. However, Christensen (2020) argues that the theoretical rationale for rejecting translation is unsound and that there is extensive empirical evidence that the brain translates across many representational formats, including motor and cognitive.

10 We won't engage in a comprehensive analysis of the DPM model here. Brozzo (2021) criticises the distinction between present- and future-directed intentions. Christensen (2020) gives an alternative account of motor representations and their interface with higher level intentions.

11 Although it should be noted that Shepherd's account includes a discussion of the role of domain norms in action evaluation which brings it closer to the problem-based conception.

12 This example reflects common advice to, and complaints about, inexperienced musicians. The following article provides a fairly typical illustration: https://www.premierguitar.com/articles/print/28953-last-call-space-is-music-too .

13 Our use of the term ‘system’ here may suggest that we have in mind a Fodorian module. However, we are adopting a different, non-Fodorian conception of modules and systems which is based in a biological framework. By ‘system’ we mean a network of structures and processes that show a degree of somewhat specific functional integration and perform one or more somewhat specific functional roles (in this case, action evaluation). A cognitive system in this sense can integrate with, and overlap with, other cognitive systems. The AES will be a ‘system of systems’ which encompasses multiple lower and higher level evaluative systems, for instance including both reward and efficiency evaluation systems.

14 In this respect our account can be contrasted with that of the predictive processing framework, which claims that behaviour is governed by a single dominant goal, namely predictive error minimization. Our account thus avoids the ‘dark room’ problem facing the predictive coding approach, which is that predictive error minimization can be achieved by placing oneself in a highly predictable environment (Sun & Firestone, 2020). We thank a referee for this point.

15 Drawing on Kath’s experience of teaching this exercise to beginner riders at skills clinics, many riders think they are almost about the graze the tyre when they are still approximately 20 cm away from reaching it. This was also true of Wayne. Kath communicated this to him verbally during the exercise, showed the gap with a hand gesture, and demonstrated the correct vs incorrect technique. In response, Wayne exaggerated his attempt at the task and was able to graze the rear tyre. Both riders were delighted!

16 Also called a ‘front wheel washout’. There are some differences in the way that ‘washout’ is conceptualised in the mountain bike community. The version of the concept Wayne was using treats it as any case in which the front wheel loses traction and slides out from underneath the rider, causing a crash.

17 This folk concept of flow should be differentiated from the concept used in psychology (Csikszentmihalyi 1990 ).

S.I.: Minds in Skilled Performance.

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Contributor Information

Wayne Christensen, Email: [email protected] .

Kath Bicknell, Email: moc.llenkcibhtak@liame .

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