Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda

  • Review Article
  • Open access
  • Published: 05 May 2021
  • Volume 34 , pages 39–71, ( 2022 )

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  • TuongVan Vu   ORCID: orcid.org/0000-0001-6700-2439 1 , 2 ,
  • Lucía Magis-Weinberg 3 ,
  • Brenda R. J. Jansen 4 ,
  • Nienke van Atteveldt 1 , 2 ,
  • Tieme W. P. Janssen 1 , 2 ,
  • Nikki C. Lee 1 , 2 ,
  • Han L. J. van der Maas 4 ,
  • Maartje E. J. Raijmakers 1 , 2 ,
  • Maien S. M. Sachisthal 4 &
  • Martijn Meeter 1 , 2  

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The question of how learners’ motivation influences their academic achievement and vice versa has been the subject of intensive research due to its theoretical relevance and important implications for the field of education. Here, we present our understanding of how influential theories of academic motivation have conceptualized reciprocal interactions between motivation and achievement and the kinds of evidence that support this reciprocity. While the reciprocal nature of the relationship between motivation and academic achievement has been established in the literature, further insights into several features of this relationship are still lacking. We therefore present a research agenda where we identify theoretical and methodological challenges that could inspire further understanding of the reciprocal relationship between motivation and achievement as well as inform future interventions. Specifically, the research agenda includes the recommendation that future research considers (1) multiple motivation constructs, (2) behavioral mediators, (3) a network approach, (4) alignment of intervals of measurement and the short vs. long time scales of motivation constructs, (5) designs that meet the criteria for making causal, reciprocal inferences, (6) appropriate statistical models, (7) alternatives to self-reports, (8) different ways of measuring achievement, and (9) generalizability of the reciprocal relations to various developmental, ethnic, and sociocultural groups.

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Introduction

In most countries, motivation for school clearly declines throughout school time (Martin, 2009 ; OECD, 2016 ; Scherrer & Preckel, 2019 ) with individual heterogeneity in changes depending on specific motivation constructs across academic domains (Gaspard et al., 2020 ; Scherrer & Preckel, 2019 ). Given this undesirable decline and the fact that motivation can be targeted by interventions, motivation has long been a central focus of educational psychology. The influence of motivation on achievement is well-documented (Burnette et al., 2013 ; Gottfried et al., 2013 ; Greene & Azevedo, 2007 ; Valentine et al., 2004 ). Yet the reverse relation is also often found, as achievement can affect motivation through experiences of success or failure (Garon-Carrier et al., 2016 ; Guay et al., 2003 ; Jansen et al., 2013 ). A common view is that both the “motivation → achievement” and “achievement → motivation” links exist and that motivation and achievement influence each other in a reciprocal manner over time (Huang, 2011 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Möller et al., 2009 ).

Researchers have been studying this reciprocal relationship between motivation and achievement for at least 20 years (Marsh et al., 1999 ). However, further insights into the nature of the relationship are currently lacking; features such as the direction of causality, behavioral mediating pathways, possible effect of the time scale, and generalizations to different motivation constructs and population groups are currently not well understood. These issues are important not just from a scientific viewpoint, but also from a practical viewpoint. To be able to design the most effective interventions aimed at improving achievement and motivation, we need to improve our understanding of the reciprocity to identify the best timing, duration, content, and appropriate target variables of such interventions, as well as other contextual factors contributing to their success.

Our objective is to summarize the current understanding of motivation-achievement interactions (drawing mainly from the academic motivation literature) and to identify the theoretical and methodological challenges that could inspire further advances to understand such specific features of this reciprocal relationship. While an exhaustive review of the literature is beyond the scope of the current paper (see the Special Issue on Prominent Motivation Theories: The Past, Present, and Future on Contemporary Educational Psychology, edited by Wigfield and Koenka, 2020 ), we start with a summary of how influential theories of academic motivation have conceptualized reciprocity between motivation and achievement, and the types of empirical evidence that have been found in support of the reciprocal relationships. In our current understanding, we have found areas of consensus, but have also identified sizable gaps. This leads to a recommended research agenda for future empirical studies on the reciprocal relations between motivation and academic achievement and suggestions on how these insights could inform future interventions.

Reciprocal Relations in Theories of Academic Motivation

Commonalities between theories.

Individual differences in academic achievement are partly the result of differences in motivation for learning (Arens et al., 2017 ; Burnette et al., 2013 ; Eccles & Wigfield, 2002 ; Guay et al., 2003 ; Huang, 2011 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Robbins et al., 2004 ; Seaton et al., 2014 ). This robust finding has spawned a wealth of theories on academic motivation and how to stimulate it. These theories differ in both substance and focus, but also have many common elements. Figure 1 represents an attempt to synthesize, for the purposes of this paper, some of the commonalities of well-established theories that have had an impact in the field of academic motivation (leaning strongly on the seminal review of Eccles & Wigfield, 2002 and adding theories that have gained traction since). Our goal is not to comprehensively review and synthesize the existing theories (although this is an urgent task, Koenka, 2020 ), but rather to illustrate how the commonalities between the theories suggest a framework in which the reciprocal relationships between motivation and achievement can be studied and understood.

figure 1

The motivation-achievement cycle, a summary model of motivation-achievement interactions, capturing some of the commonalities within prominent theories of academic motivation. Blue boxes denote motivation constructs, green (dotted) arrows behavioral intermediaries (quality of learning and quantity of learning), and yellow boxes and arrows denote achievement-related constructs (flow and perceived performance). Gray arrows denote outside influences that are themselves not part of motivation-achievement interactions (e.g., cultural and social influences that affect both expectancies and values)

Motivation has up to 102 definitions (Kleinginna & Kleinginna, 1981 ), but is often seen as a condition that energizes (or de-energizes) behaviors. In many theories, motivation results from what can be called an appraisal of the behavior that one is motivated to perform (the word appraisal is rarely used with regard to motivation, but the processes described are akin to those captured in the emotion literature). In that appraisal, two elements are combined (Eccles & Wigfield, 2002 ): the value attached to the behavior and its outcomes, and the expectancy of the likelihood of certain outcomes of the behavior. These two sides, expectancy and value, are explicit in expectancy-value theory (Eccles & Wigfield, 2002 , 2020 ), attribution theory (Graham, 2020 ; Weiner, 2010 ), control-value theory (Pekrun, 2006 ; Pekrun et al., 2017 ), and Dweck’s integrative theory (Dweck, 2017 ).

Many other theories focus either on the value attached to behavior or on expectancies. Theories on the values side of the ledger (goal theories, flow theory, self-determination theory, individual differences theories, and interest theories) focus on interest, goals, needs for relatedness, competence, and autonomy. Theories on the expectancy side, notably self-efficacy theory, control theories, social-cognitive self-regulation theories, and the process-oriented metacognitive model, focus on how students’ beliefs (or perception ) about their competence and efficacy (i.e., academic self-concept, see below), expectancies for success or failure, and sense of control over achievement affect motivation. Different constructs have been studied that tap into these beliefs underlying one’s expectancies, such as academic self-concept, self-efficacy, locus of control, and perceived control.

A motivation construct frequently used to study the reciprocal motivation-achievement relationship is academic self-concept (hitherto, ASC, discussed in further details in section “Different motivation constructs” below) which is how individuals evaluate their ability specifically in an academic domain (Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Shavelson et al., 1976 ). ASC is a component distinct from physical, social, and emotional self-concepts within the multidimensional, hierarchical model of self-concept (Marsh & Craven, 2006 ; Marsh & Martin, 2011 ). ASC is itself also multidimensional and usually measured by the Self Description Questionnaire (Marsh et al., 1999 ; Marsh & O’Neill, 1984 ); its academic subscales tap into general academic self-concept, math self-concept, and verbal self-concept. Much empirical research on motivation-achievement interactions operationalizes motivation as ASC in a certain academic domain, most often in mathematics and verbal subjects such as language and reading (Guay et al., 2003 ; Seaton et al., 2014 ); for meta-analyses and reviews, see Burnette et al. ( 2013 ), Eccles and Wigfield ( 2002 ), Marsh and Craven ( 2006 ), Marsh and Martin ( 2011 ), and Robbins et al. ( 2004 ).

It is worth noting that many theories posit that beliefs about the self (including self-concept and self-esteem and mindset/implicit theory of self attributes) are important causes of human behavior and learning (Bandura, 1997 ; Carver & White, 1994 ; Deci & Ryan, 2000 ; Molden & Dweck, 2006 ). Although the idea that ASC or other beliefs about the self affect achievement has been challenged (see the discussion in Marsh & Craven, 2006 ), there has also been much empirical research in support of it (Burnette et al., 2013 ; Gottfried et al., 2013 ; Greene & Azevedo, 2007 ; and the meta-analyses of Huang, 2011 ; Valentine et al., 2004 ). One suggested pathway is that positive self-beliefs can lead to self-affirmative, self-regulatory, academic behaviors (or achievement behaviors , see below) such as exerting effort, demonstrating persistence, and selecting goals that are conducive to the achievement of academic goals.

Another pathway for beliefs about the self to act as a causal agent on academic achievement, according to self-worth theory (Covington, 2000 ), is that students with positive beliefs about themselves assign high and positive values to academic activities. Academic activities are then viewed as important, intrinsically interesting, of high expected utility and of low cost, which leads to high achievement (Valentine et al., 2004 ). Also, in self-determination theory, feelings of competence are a precursor of intrinsic motivation, again leading to a higher value being assigned to academic activities if one feels competent. This would then lead to behaviors that support later achievement. A recent study of more than 30,000 college students found that need for competence (relative to need for autonomy and relatedness) is the strongest predictor of perceived learning gains (Yu & Levesque-Bristol, 2020 ).

An appraisal of values and expectancies leads to the decision to engage (Cleary & Zimmerman, 2012 ; Kuhl, 1984 ; Schunk & DiBenedetto, 2020 ). According to the self-regulatory account of motivation (Cleary & Zimmerman, 2012 ; Schunk & DiBenedetto, 2020 ), students first identify values and expectancy of learning activities, then engage in self-regulatory processes (self-instruction, attention focusing, task strategies, etc.). Following their performance, students conduct self-evaluations, infer causal attributions, and make adaptive or maladaptive attributions of their successes and failures. This account stresses the importance of metacognition, where students who can monitor their learning processes can then maintain their engagement in the learning cycle.

The appraisal of values and expectancies can also trigger academic emotions, such as pride in achievement, hope, boredom, and enjoyment. Control-value theory (Pekrun, 2006 ; Pekrun et al., 2017 ) describes how such emotions codetermine what are termed achievement behaviors —behaviors that are conducive to the achievement of academic goals. In line with dominant theories of emotion (e.g. Frijda, 1988 ; Lazarus, 1999 ), Pekrun ( 2006 ) assumed that an appraisal of control of the learner and the value of learning activities lie at the basis of academic emotions. For example, if a learner values an academic outcome and believes it is somewhat under his or her control, he or she may feel the emotion of hope. While it is not certain that the same kinds of appraisal lie at the basis of both motivation and academic emotions, it would seem plausible and parsimonious. Indeed, Pekrun ( 2006 ) suggested that this is the case, though he cautioned that more research is needed.

Figure 1 may raise the question of what actually distinguishes motivation from emotions, since both seem to result from an appraisal of the situation, and both energize or de-energize certain behaviors. This is a valid question, and Kleinginna and Kleinginna ( 1981 ) already noted that a sharp line between motivation and emotion is difficult to draw (also see Berridge, 2018 ). Emotions will typically be more temporary than motivation, but this is a fuzzy distinction. Emotions and motivation may also interact. Emotions may for example make a learner assign more or less value to academic activities, or may change the learner’s expectations around their chances of success or failure, which then changes the appraisal that underlies motivation. Literature showing that emotions and academic achievement also form reciprocal relationships over time has recently emerged (Putwain et al., 2018 ).

Pathways from Motivation to Achievement and Vice Versa

While it is generally accepted that motivation affects achievement, it is not completely clear how . Theoretically, two routes can be discerned (see Fig. 1 ). The first is the quantity (frequency and intensity) of academic behaviors aimed at achievement (such as effort, persistence, etc.) (Cury et al., 2008 ; Dettmers et al., 2009 ; Doumen et al., 2014 ; Marsh et al., 2016 ; Pinxten et al., 2014 ; Plant et al., 2005 ; Trautwein et al., 2009 ). As a second route, higher levels of motivation could also be associated with higher quality of academic behaviors; for example, by adopting effective learning strategies, adaptive meta-cognitive strategies, spaced practice, elaboration, retrieval practice, interleaving, dual coding, and so on. Several theories of academic motivation support the idea that higher motivation leads to higher quality behaviors. Both intrinsic motivation (self-determination theory, Deci & Ryan, 2000 ) and interest (interest theories, Alexander et al., 1994 ) have been linked to deeper learning (Alexander et al., 1994 ; Schiefele, 1999 ; Scott Rigby et al., 1992 ). Positive academic motivations have also been suggested to facilitate creative learning strategies (control-value theory, Pekrun, 2006 ), and incremental implicit beliefs (growth mindset) to facilitate mastery-oriented strategies (Burnette et al., 2013 ).

Effects of achievement on motivation may also take two routes. The first is through perceived achievement. Many theories, such as self-efficacy theory (Bandura, 1997 ), expectancy-value theory (Eccles & Wigfield, 2002 ), control theories (Skinner, 1995 ), and attribution theory (Weiner, 2010 ) explicitly suggest that past achievement leads a learner to experience feelings of self-efficacy and perception of control. What matters most in this regard is the learner’s own evaluation of this outcome, for which we use the term perceived performance in Fig. 1 . High perceived performance will thus change the expectancies of learners (i.e., make them trust that good outcomes are attainable), but it may also alter the value attached to learning activities. For example, in self-determination theory, the feeling of competence (strengthened by positive perceived achievement) is a basic need that increases the intrinsic value of learning.

The second route from achievement to motivation is central to flow theory (Csikzentmihalyi, 1990 ). An activity in which the learner is holistically immersed can generate a feeling of flow, which is rewarding in its own right and alters the value attached to the academic behaviors.

External Factors Affecting Motivation, Effort, and Achievement

Figure 1 suggests a positive feedback loop, with motivation feeding achievement, and achievement feeding motivation—an idea that is alluded to in some theories (Cleary & Zimmerman, 2012 ; Eccles & Wigfield, 2002 ; Schunk & DiBenedetto, 2020 ). Most explicit in this regard is the self-regulatory account of motivation (Cleary & Zimmerman, 2012 ) where the pathway between self-regulation and achievement is a cyclical feedback loop. Schunk and DiBenedetto ( 2020 ) suggest an iterative process between perceived progress, self-efficacy, and goal pursuit. Bandura’s social cognitive theory also stresses the reciprocity of the interactions between behavioral, environmental, and personal factors (Bandura, 1997 ). Crucially, this raises the question of how such a positive feedback loop could get started, and how, once started, it could lead to any other outcome than either perfect motivation and achievement, or negative motivation and failure. The answer to those questions may rest in the external influences on motivation and achievement. These are indicated in Fig. 1 by the gray arrows:

Extrinsic rewards and requirements tied to achievement, e.g., schools or parents, may change the value attributed to academic behavior, and so change motivation. Although this has been described in self-determination theory as potentially detracting from intrinsic motivation (Deci & Ryan, 2000 ), it may also jolt a motivation-achievement cycle that would otherwise not start (Hidi & Harackiewicz, 2001 ). Supporting autonomy and creating relatedness are other ways in which external actors can increase the value attached to learning, increasing motivation and achievement (Deci & Ryan, 2000 ).

Cultural norms (described in control theories and control-value theory, Pekrun, 2006 ; Skinner, 1995 ), social learning, and verbal persuasion by others (social cognitive theory, Bandura, 1997 ) can alter the expectations, values, and attributional processes of learners (expectancy-value theory, attribution theories, Eccles & Wigfield, 2020 ; Graham, 2020 ), and therefore keep a motivation-achievement cycle going that would otherwise falter or not start up.

Effort is not only a result of the learner’s motivation but also of outside requirements (e.g., deadlines and exams set by the educational institution, Kerdijk et al., 2015 ). Such outside requirements can lead to achievement in the absence of strong motivation.

Quality of learning is not only affected by motivation but also by the abilities of the learner and the quality of teaching, instructions, and study materials. Thus, achievement can increase in the absence of stronger motivation, because of better support for learning.

Perceived achievement is not only determined by true achievement but also by elements of educational design, such as the form in which feedback is given (e.g., a grade that either accentuates the ranking of the student or the degree to which the study material was mastered, or feedback on effort instead of performance, De Kraker-Pauw et al., 2017 ). Perceived achievement is also subject to interpretative, comparison, and attributional processes (described in attribution theories, Graham, 2020 ; Weiner, 2010 ). This means that true high achievement can still fail to support motivation (e.g., when a sibling performs even better), or low achievement can be viewed in such a way so as to not be detrimental for motivation.

Such external factors are not only important for a complete causal understanding of motivation-achievement interactions (i.e., highly relevant for educational researchers) but also because they offer entry points for interventions that enhance motivation, achievement, or both (i.e., highly relevant for educators).

What Avenues for Empirical Research Have Been Explored?

Figure 1 shows that theories of academic achievement imply a reciprocal relationship between motivation and achievement. A comprehensive review of studies is beyond the scope of this manuscript (see narrative reviews and meta-analyses) (Huang, 2011 ; Marsh & Craven, 2006 ; Scharmer, 2020 ; Valentine et al., 2004 ; Valentine & Dubois, 2005 ), but we will review the kinds of evidence that have been brought to bear in support of such reciprocal relationships. Analyzing this evidence allows future directions on the field to be charted.

The earliest support for the relationship between motivation (focusing specifically on self-concepts and other self beliefs) and academic achievement comes from cross-sectional and correlational studies, reviewed by Hansford and Hattie ( 1982 ). These studies established a relationship between self-concepts and academic achievement, but no causal paths. Subsequent work set out to investigate the causal and temporal ordering of the effects using structural equation models (SEMs) and longitudinal data (e.g., Marsh et al., 1999 ). To date, the majority of evidence for the reciprocal relationship between self-concept and achievement has come from such time-series or cross-sectional data collected at schools, to which various SEMs have been fitted (see Marsh & Craven, 2006 for a narrative review and Huang, 2011 for a meta-analysis of such studies).

More recent studies showcase impressive efforts of researchers to use large sample sizes and longitudinal data of up to six waves, allowing changes in motivation and achievement of students to be tracked across their school career (e.g., Marsh et al., 2018 ; Murayama et al., 2013 ). A recent meta-analysis (Scharmer, 2020 ), which includes such studies that were published between 2011 and August 2020, showed that overall, the pooled effect of achievement on motivation was twice (β = .12) the pooled effect of motivation on achievement (β = .06), though both are what is conventionally considered a small effect. These findings are in line with Valentine and DuBois ( 2005 ) who found that academic achievement had a stronger effect on self-belief than vice versa. In contrast, Huang ( 2011 )’s meta-analysis found a slightly larger effect of self-concept on achievement than the other way around. Valentine and DuBois ( 2005 )’s findings were also more similar to Scharmer’s ( 2020 ) in terms of the size of the effects (achievement on self-belief: β = 0.08; self-belief on achievement: β = 0.15). Huang ( 2011 ), however, found considerably larger ranges of effects overall (achievement on self-concept: β = 0.19–0.25; self-concept on achievement: β = 0.20–0.27).

There have also been interventions and randomized controlled field studies in which either self-concept or other motivation constructs were manipulated (e.g., Savi et al., 2018 ; Vansteenkiste et al., 2004 ), thereby allowing for causal inferences. The meta-analysis of these studies by Lazowski and Hulleman ( 2016 ) showed that, while interventions targeting motivation usually led to positive outcomes on achievement (medium effect size; average Cohen’s d of 0.49), it did not matter which theory was at the basis of the intervention— all theories of motivation performed about equally well. However, experimental studies that look at the reverse causal path, manipulating achievement (or the perception of achievement) to affect motivation, are scarce. One example is an intervention study by Betz and Schifano ( 2000 ) where students were ensured of successful completion of a task followed by affirmation of their accomplishments with applause and verbal praise. This resulted in an increase in self-efficacy (a motivation construct highly related to ASC, Bong & Skaalvik, 2003 ). Nevertheless, to the best of our knowledge, few studies have done both: combining experimental manipulation and longitudinal design to investigate reciprocal motivation-achievement relations (an exception that we are aware of is Bejjani et al., 2019 which will be discussed later).

Research Agenda

The overview given above suggests that empirical evidence for reciprocal relations between motivation and achievement exists. However, several features of such relationships are still poorly understood. Also, some doubts about the robustness of the effects have recently surfaced (which we discuss in detail in section “Choice of appropriate statistical models” below). In other words, there are still unanswered theoretical and empirical questions about the reciprocal relationship between motivation and academic achievement. Below, we outline these issues and a research agenda for future research that can answer these remaining questions. These are organized into questions pertaining to theoretical lacunae, methodological challenges, and questions about the scope of theories and the generalizability of empirical results.

Theoretical Lacunae

Multiple motivation constructs.

First, as we presented above, many motivation theories have implicitly or explicitly conceptualized the relationship between a plethora of motivation constructs and achievement as reciprocal. However, to date, a large amount of empirical research on reciprocal motivation-achievement interactions has mainly studied ASC (Arens et al., 2019 ; Brunner et al., 2010 ; Chen et al., 2013 ; Dicke et al., 2018 ; Gottfried et al., 2013 ; Grygiel et al., 2017 ; Guay et al., 2003 ; Guo et al., 2015 ; Möller et al., 2011 ; Niepel et al., 2014a , 2014b ; Retelsdorf et al., 2014 ; Viljaranta et al., 2014 ; Walgermo et al., 2018 ; for meta-analyses and reviews, see Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Valentine et al., 2004 ; Valentine & Dubois, 2005 ) . This raises the question of whether findings generalize to other motivation constructs that are related yet could also have a distinctive reciprocal relationship with academic achievement.

Moreover, although the studies involving ASC were groundbreaking attempts to show reciprocal relations, there are several reasons why future studies should contemplate using different motivation constructs other than ASC. First and foremost, ASC and achievement are highly intertwined, as items in ASC questionnaires usually ask students to report on their achievement (e.g., “I get good marks in most academic subjects,” “I learn quickly in most academic subjects” (Marsh & O’Neill, 1984 ). Fulmer and Frijters ( 2009 , p. 228) in their critique of how motivation is measured in educational psychology also made the point that “self-report measures confound the measurement of motivation with other variables, such as ability and attention.”

Second, a meta-analysis investigating mean-level changes of a number of important motivation constructs concluded that the decline in motivation shows non-trivial differences across these constructs (Scherrer & Preckel, 2019 ). An important implication of this finding is that more attention should be paid to differentiation among multiple motivation constructs in future empirical studies.

Third, ASC might also be less malleable than other motivation constructs since general self-concept is relatively stable—especially for those at lower levels (Scherbaum et al., 2006 ). Research into the Big-Fish-Little-Pond phenomenon (i.e., students in high-achieving classes having lower ASC than those with comparable aptitude in regular classes) suggests that domain-specific ASC (more so than general ASC) is influenced by social comparison (Fang et al., 2018 ; Marsh et al., 2018 ). Nevertheless, it may be hard to manipulate ASC in a randomized controlled trial (although it has been indirectly done by affirming general self-esteem and personal values, Cohen et al., 2009 ). Other motivation constructs that can be modified through external influences (e.g., situational interest, perceived control, etc.) might yield useful guidance for designing interventions.

Furthermore, the heavy focus on ASC may reflect an emphasis on a cognitive, intrapsychological theoretical view of motivation while losing sight of social, contextual, historical, and environmental factors that arguably also play important roles (see the Special Issue on Prominent Motivation Theories: The Past, Present, and Future on Contemporary Educational Psychology, edited by Wigfield and Koenka, 2020 ). Last but not least, ASC is mainly self-reported and, despite the availability of well-constructed measures, it suffers from all the caveats inherent to self-report measures (see section “Alternatives to self-reports” below).

Given that there are other well-studied motivation constructs such as achievement goals, self-efficacy, interest, and intrinsic motivation (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ), further research with multiple non-ASC motivation constructs included as concomitant predictors of academic achievement is therefore much needed. In recent investigations of the reciprocal relationship between motivation and achievement, motivation constructs other than ASC have started to be included (e.g., self-efficacy in Grigg et al., 2018 ; Schöber et al., 2018 ; achievement goals in Scherrer et al., 2020 ; intrinsic motivation in Hebbecker et al., 2019 ; and interest in Höft & Bernholt, 2019 ). Yet, these studies are still small in number. Twenty-four out of 41 studies included in the meta-analysis of Scharmer ( 2020 ) still used ASC as the main motivation construct of interest.

Behaviors as Mediating Factors in the Motivation → Achievement Link

As mentioned above, theories of academic motivation imply several pathways through which motivation influences achievement and vice versa (see Fig. 1 ). For the motivation → achievement link, the rationale is that motivation leads to active and effortful commitment to learning (e.g., E. Skinner et al., 1990 ), implying that motivation constructs that are beliefs about competence and efficacy influence achievement by inducing self-regulatory, academic behaviors. In a similar vein, the volition theory of motivation (Eccles & Wigfield, 2002 ; Kuhl, 1984 ) posits that motivational beliefs only lead to the decision to act. Once the individual engages in action, volitional processes are required and determine whether the intention is fulfilled. Thus, self-regulatory processes theoretically mediate the link between beliefs and accomplishment of the task.

However, there is a relative paucity of empirical research and especially longitudinal studies that include measures of such regulatory processes. Usually, when studies found reciprocal relations between ASC and other motivation constructs and achievement, they left unanswered which pathways mediate the link between such beliefs and achievement (Marsh & Martin, 2011 ). To our knowledge, initial attempts to study mediating processes in longitudinal designs (Marsh et al., 2016 ; Pinxten et al., 2014 ; Trautwein et al., 2009 ) yielded mixed findings with regards to the role of effort in the relationship between ASC and academic achievement. This may be due to the fact that there are multiple operationalizations and evaluations of the construct effort (Massin, 2017 ), which may have varying relations with academic achievement. Specifically, Marsh et al. ( 2016 ) and Pinxten et al. ( 2014 ) measured subjective effort—i.e., students were asked to rate their own effort expenditure. Students might perceive that having to try hard (i.e., expending a great deal of effort) is indicative of a lack of academic ability (Baars et al., 2020 ). Subjective effort, as opposed to objective effort, might therefore have a very different relation to motivation and achievement.

In non-longitudinal studies looking at the relations between academic motivation and achievement, the evidence on behavioral mediators also shows differentiation related to how effort is measured. When effort is measured as quality of learning (e.g., selecting adaptive goals, adopting higher-quality learning strategies, etc.), there is some evidence for a positive link between academic achievement and effort (Trigwell et al., 2013 ). However, when effort is measured as a quantity of learning (such as study time, practice time, time-on-task, persistence, etc.), this relationship seems either weak or only significant after controlling for quality of learning (Cury et al., 2008 ; Dettmers et al., 2009 ; Doumen et al., 2014 ; Plant et al., 2005 ) or even negative (the labour-in-vain effect, Koriat et al., 2006 ; Nelson & Leonesio, 1988 ; Undorf & Ackerman, 2017 ). This provides suggestions for future attempts to parse the mediating factors in the motivation → achievement link in reciprocal relations between these two constructs. It is most fruitful to measure subjective and objective measures of quantity and quality of learning (and use triangulation of methods, as strongly suggested by Scheiter et al., 2020 ) and compare their effects on academic achievement.

Irrespective of what operationalization is chosen, it is important to note that it is not trivial to evaluate and conceptualize effort (see extensive discussions in Baars et al., 2020 ; Scheiter et al., 2020 ). Is effort the allocation of cognitive control, i.e., mental effort (Kool & Botvinick, 2018 ), or the intention to think deeply, regardless of the amount of time spent (Haynes et al., 2016 ), or a preference for thinking hard (Beck, 1990 ), a decision process rather than a capacity or resource that is physically limited (Gendolla & Richter, 2010 )? Yet, only by measuring regulatory processes that mediate the motivation → achievement pathway, we can make progress in understanding the underlying mechanism of mutual influences between motivation and achievement.

Mutualistic Perspective and the Network Approach

Next, studies have typically investigated relations between one or a small number of motivation constructs (e.g., ASC and interest, Walgermo et al., 2018 ). The discussion above and Fig. 1 show that multiple motivation constructs are linked to academic achievement, which may also all be mutually related. Like many topics in psychology, there is a huge overlap in terms and variables in the literature on motivation and achievement; the same construct may have different names, or different constructs go under the same name (this is known as Jingle-Jangle fallacies; e.g., Marsh et al., 2003 ). One possible solution to the Jingle-Jangle fallacies with regard to motivation was proposed by Marsh et al. ( 2003 ), who presented a factor model with two higher-order factors (dubbed learning and performance ) that explained relations between motivation constructs. In this approach, assumptions on the number of factors and factor structure are necessary.

The network approach is different; it does not assume an a priori structure of motivation factors. Instead, it uses the (bidirectional) partial correlations between variables in empirical data and in doing so clusters of variables which can be interpreted as constructs may emerge. The idea of a network of mutual relations to model psychological constructs was introduced by van der Maas and colleagues (van der Maas et al., 2006 , 2017 ) as an explanation for the positive correlations (the positive manifold) between intelligence sub-test scores. This led to a productive area of research with applications in many areas of psychology (Dalege et al., 2016 ; Robinaugh et al., 2020 ; Sachisthal et al., 2019 , 2020 ; Zwicker et al., 2020 ). The general hypothesis in psychological network models is that correlations between observed behaviors, such as cognitive functions, psychopathological symptoms, and attitudes (or, motivation constructs ), are not due to unobserved common causes, but to a network of interacting psychological, social, and/or biological factors. These observed behaviors are the nodes in the network and the partial correlations are the edges.

An example of how such a network approach can be applied to the area of motivation can be found in a study of interest in science (Sachisthal et al., 2019 ). This study included measures of students’ value of science, their science engagement, and achievement. The correlations between these measures were modeled as a network, within which clusters of variables emerged. These can be seen as empirically derived constructs, replacing the at times arbitrary theoretical separation between (motivation) constructs. Given that in motivation research many constructs with considerable overlap exist (Anderman, 2020 ; Hattie et al., 2020 ), such empirically derived concepts may prove especially relevant.

Within this network, variables with the strongest direct relationships can be identified. A positive change in a central variable should lead to a positive change throughout the network and these central variables may differ between contexts. For example, enjoyment emerged as the central node in the network of Dutch students, whereas engagement behaviors emerged as central in the network of Colombian students and therefore different approaches for increasing science interest are advised for the two countries (Sachisthal et al., 2019 ). Central variables may be efficient intervention targets as interventions informed by network analyses have been shown to be highly effective as these central variables were later shown to be predictive of subsequent behaviors (e.g., Sachisthal et al., 2020 ). Moreover, further support for this assumption comes from a recent study by Zwicker et al. ( 2020 ) who identified guilt as the central node in the network of attitude and environmental behaviors. They then successfully manipulated guilt which increased willingness to engage in such behaviors.

In sum, these works exemplify how network approaches can be used (1) to model distinctive but highly related motivation and achievement constructs simultaneously and map their relations and (2) to derive hypotheses about which included constructs may be efficient targets for interventions (see Borsboom, 2017 , for an overview). Moreover, the fact that network analyses found different central variables in different populations also showcases how such an approach can flexibly capture interactions between motivation factors in real life. Last but not least, at a more abstract level, a mutualistic network approach can potentially solve the question of the mechanisms of the impact of motivation on achievement (also raised in Hattie et al., 2020 as an important avenue for future research). Specifically, how clusters of motivation constructs, behavior, and achievement interact with one another can be modeled, and how reciprocal relations between them arise over time. This can only be achieved when multiple motivation constructs are measured in one single study (as argued above in section “Multiple motivation constructs”).

Time Scale of the Interactions (Short vs. Long Cycle)

Another gap in the literature that we identified is that much research on the reciprocity between motivation and achievement has been done with data collected at large time intervals, which reflect changes that happen over months or years (e.g., Harackiewicz et al., 2008 ; Marsh et al., 2005 , 2016 ; Nuutila et al., 2018 ). For example, it is common for studies to include data collected per academic semester or year (e.g., Gottfried et al., 2013 ); sometimes, other time intervals have been used, such as weeks (e.g., Yeager et al., 2014 ). However, theories of motivation such as self-determination theory or expectancy-value theory are not formulated with an explicit time scale, and the interactions they describe seem framed in terms that suggest that the effects of motivation constructs happen without delays (i.e., on a time scale of seconds). Recent accounts of motivation are situated ones, which also call attention to fine-grained, moment-to-moment fluctuations that occur during learning engagement (Schunk & DiBenedetto, 2020 ). This raises the question how such fast dynamics can be captured if constructs are measured with large time lags in between.

It is possible that there are interactions between motivation and achievement at both short and long timescales, and that these are qualitatively different. We will refer to these hypothetical interactions at different time scales as short (or fast) and long (or slow) cycles between motivation and achievement. Some constructs may change in slower cycles (e.g., achievement goal orientation, mindset, academic self-concept) than others (e.g., autonomy, or even faster: emotions). In research focusing on interest and achievement emotions, for instance, a stable, so-called trait level (e.g., individual interest) is often distinguished from a shorter, task-dependent state level (e.g., situational interest) (see Hidi & Renninger, 2006 ; Renninger & Hidi, 2011 for interest; Pekrun, 2006 for achievement emotions). Nesselroade’s ( 1991 ) model of within-person psychological change also distinguishes between state and trait. The former is rapid and potentially more easily reversed than the latter. Developmental processes are thought to underlie trait constructs, for instance suggesting that the repeated experience of a positive state (i.e., enjoyment) will lead to a positive trait value. While it has been suggested that reciprocal relations play a more central role on the trait level—e.g., explaining the stronger relations between emotion antecedents and emotions (Bieg et al., 2013 ), studies investigating reciprocal relations are still missing at a state (or task) level . Furthermore, the difference between slow and fast change is also more salient for certain constructs than for others. For example, in one rare study where the within-task changes in multiple motivation constructs was studied, researchers found that while students’ self-efficacy generally grew throughout the progress of a task, interest did not (Niemivirta & Tapola, 2007 ). This suggests that when studies do not consider fast vs. long cycles of constructs, the effects of a faster changing variable on a slowly changing variable can be missed.

The remedy to these problems is to consider using data collected at either diverse time intervals or with theoretically informed time intervals to capture the ebbs and flows of the relations between constructs over time and their corresponding short and long cycles (Duff et al., 2015 ; McNeish & Hamaker, 2019 ). In addition, special attention should be paid to “short cycles”—especially since fast-changing constructs may be more effective targets for interventions. Intensive longitudinal designs can help uncover potential “real-time” causal variance attributable to a construct that would be missed when it is measured at relatively lengthy intervals such as one academic semester or year (McNeish & Hamaker, 2019 ). This may also help when developmental trajectories are characterized by non-linear trends that cannot be captured by low-frequent measurements (McNeish & Hamaker, 2019 ). A deliberate choice of time intervals and the use of non-questionnaire measures will also be helpful in this respect (see section “Alternatives to self-reports” below).

A related but distinguishable issue is the stability of the reciprocal relation between motivation and achievement. Whether or not reciprocal effects of motivation and achievement are stable across school careers is a question with significant theoretical and practical consequences (Marsh et al., 2018 ). Two recent studies found motivation declines to be associated with particular academic stages, for example some constructs such as achievement goal orientation specifically dropped in the transition to secondary school (Scherrer et al., 2020 ). The Scherrer et al. ( 2020 ) data are however among the first longitudinal attempts that can reveal how such declines could potentially impact the reciprocity between motivation and achievement. Theoretically, one could assume that the impact of motivation on achievement is low early in a new environment (e.g., a school transition) where learners experience considerable uncertainty regarding their competence and academic standing (Eccles et al., 1993 ; Valentine et al., 2004 ). When the learning environment is stable, the impact of achievement on subsequent motivation might be more substantial. Some support for such a pattern is provided in Scherrer et al. ( 2020 ) who found the reciprocal effects only in later time points and not in earlier time points after transition into secondary school. However, these studies were not designed specifically to test the transition vs. non-transition contrast, prompting the need for subsequent longitudinal studies that focus on the effect of school transition (to our knowledge, Rudolph et al., 2001 is among the first but only has two waves of data).

Methodological Challenges

When extant research finds the relationships between motivation and achievement, the interpretation with regards to causal relations remains difficult due to the lack of experimental manipulation (Granger, 1980 ; Holland, 1986 ; Marsh et al., 2018 ; Mega et al., 2014 ). In almost every study investigating reciprocal motivation and achievement relations, the need for experimental designs in which either motivation or achievement is manipulated is raised as a suggestion for future research (Marsh et al., 2016 , 2018 ; Mega et al., 2014 ; Pinxten et al., 2014 ). The term “effect” in many existing studies is used only in “conventional statistical sense and standard path analytic terminology, as representing a relation that is not necessarily causal” (Marsh et al., 2018 , p. 268).

Research that aims to establish causality in the reciprocal relationship between motivation and achievement would need to meet three preconditions. The first precondition of causality is order , that is “x must precede y temporally” (Antonakis et al., 2010 , p. 1087). Causality of reciprocal effects requires both orders (x precedes y, y precedes x), as well as alternations of x and y (x precedes y, which is again followed by x). The pale blue (with solid outline) squares in Fig. 2 show this alteration of measurements of motivation and achievement. The top pale blue rectangle starts with motivation, whereas the bottom starts with achievement. The second precondition is correlation : “x must be reliably correlated with y (beyond chance)” (Antonakis et al., 2010 , p. 1087).

figure 2

Representation of three types of study designs that can investigate the relationships between motivation and academic achievement. (1) The gray box shows that to establish that motivation causes academic achievement (top) or vice versa (bottom), experimental manipulation is needed, intervening on the predictor at time point 1, which influences the outcome at time point 2 and so on. The straight thin arrows are the cross-lagged relations and the curved arrows the autoregressive relations. (2) The light blue boxes (top and bottom) illustrate the types of design where reciprocity but not necessary causal effects between motivation and achievement can be established. (3) The green boxes (top and bottom) show the type of design that can investigate both reciprocity and causality between motivation and achievement (i.e., a study where experimental manipulation is included and reciprocal relationships are measured). t time-point, M motivation, A achievement

Several studies with high quality and quantity of longitudinal data meet these two pre-conditions (e.g., Arens et al., 2017 ; Bossaert et al., 2011 ; Chamorro-Premuzic et al., 2010 ; Chen et al., 2013 ; Collie et al., 2015 ; Dicke et al., 2018 ; Grygiel et al., 2017 ; Hebbecker et al., 2019 ; Höft & Bernholt, 2019 ; Marsh et al., 2016 , 2018 ; Miyamoto et al., 2018 ). In these studies, autoregressive paths (the curved arrows in Fig. 2 , which go from measurement of a variable at one time point to the measurement of the same variable at the next time point) and cross-lagged paths (the straight arrows in Fig. 2 , which go from measurement of a variable at one time point to the measurement of a different variable at a later time point) are found. In other words, autoregressive paths represent the direct effects of variables on themselves over time and cross-lagged paths the direct effects of two variables on each other over time. Such cross-lagged paths show the reciprocity between the variables but not necessarily causality in these relations (Usami et al., 2019 ). Correlation between different variables, measured at different time points, is a necessary but not sufficient requirement of causality in mutual relations. Establishing causality of reciprocal effects requires the experimental manipulation of at least one of the two variables.

Importantly, to our knowledge, no studies of the mutual relations between motivation and achievement also satisfy the third precondition of causality, that is the manipulation of x has an effect on y at a later time point, followed by (a) repeated measure(s) of x (and y) (Antonakis et al., 2010 ). In Fig. 2 , manipulation is indicated by the thick arrow. In the upper panel of Fig. 2 , the manipulation of motivation affects achievement in the gray (with dash outline) part of the figure. If the manipulation is followed by an alteration of the variables with cross-relations, the findings would support causality of motivation in reciprocal relations between motivation and achievement. We searched for such studies in meta-analyses of interventions (Harackiewicz et al., 2014 ; Lazowski & Hulleman, 2016 ; Sisk et al., 2018 ), in the latest meta-analysis of longitudinal studies (Huang, 2011 ) and Scharmer ( 2020 ). We encountered two studies that contained both an experimental manipulation of a motivation construct and subsequent multiple, alternate measurements of motivation and performance. Cohen et al. ( 2009 ) found that structured writing assignments to prompt African American students to reflect on their personal values (i.e., self-affirmation interventions) resulted in improved academic achievement (GPA), as well as self-perception and an increased rate of remediation, in the following school year for low-achieving African Americans. Yeager et al. ( 2019 ), in a large-scale mindset intervention, also had more than one wave of manipulated motivation and measurement of achievement. Although the authors discuss the role of a recursive process Yeager & Walton, 2011 ) , neither of these interventions modeled reciprocal effects between motivation and performance (Cohen et al., 2009 ; Yeager et al., 2019 ).

In the lower panel of Fig. 2 , the arrow indicates manipulation of achievement. A manipulation of achievement that affects motivation, which is again cross-related to achievement, would support a causal effect of achievement in reciprocal relations between achievement and motivation. However, it is hard to manipulate achievement independently from motivation. For example, manipulations of instruction, modeling, practice, and self-correction may all affect achievement, but they may do so partly by making the material more appealing, raising motivation at the same time or before achievement is raised. New manipulations are needed that raise, for example, perceived performance without raising performance per se, as a way to circumvent such issues. For causal inferences, experiments would ideally include (double-blinded) random assignment, which is possible in the lab but poses important practical problems in the classroom (cf. Savi et al., 2018 ). In sum, future research with the types of studies that can investigate both reciprocity and causality between motivation and achievement would be highly valuable.

Choice of Appropriate Statistical Models

Although the existence of the reciprocal relationship between motivation and performance is generally agreed upon, there are also empirical works that fail to establish such a relationship (Fraine et al., 2007 ) or cast doubts on the robustness of the reciprocal effects (Burns et al., 2020 ; Ehm et al., 2019 ). Such studies most importantly also point out that the choice of sophisticated statistical models to investigate such relationships can have implications for the conclusion drawn (e.g., Burns et al., 2020 ; Ehm et al., 2019 ). Ehm et al. ( 2019 ) specifically found that although a cross-lagged panel model (CLPM) supported reciprocal motivation-achievement relations, other models did not—such as the random-intercept CLPM, which Hamaker et al. ( 2015 ) showed to be more effective than CLPM in explicitly modeling within- and between-individual changes across time. In addition, as Usami et al. ( 2019 )—in their comprehensive unified framework of longitudinal models—demonstrated, it is important to identify the existence of third time-varying or time invariant variables (such as stable traits) that can have a causal effect on the longitudinal relationship but are yet accounted for in a model. Substantial knowledge about such confounders will help researchers select the correct statistical model. Again, this issue is closely related to the short vs. long cycle of the constructs discussed above.

Alternatives to Self-Reports

Most studies investigating reciprocal relationship between motivation and achievement have measured motivation through questionnaires probing ASC (e.g., the Academic Self-Description Questionnaire by Marsh & O’Neill, 1984 ). Despite their evident psychometric benefits, self-reports (including questionnaires) of motivation suffer from many inherent caveats. Fulmer and Frijters ( 2009 ) list several that are relevant. First of all, questionnaires are subjective and rely on the assumption that motives are consciously accessible, declarative, and communicable to other people, while as discussed above, motivation arises from partially inaccessible and non-declarative cognition and emotions. Students may also differ in their capacity to reliably answer the questions (e.g., consider alexithymia—a psychological trait that is characterized by difficulties with verbalization of one’s own emotions and psychological introspection, Lumley et al., 2005 ). Second, the lack of rigor in the conceptualization of motivation constructs often becomes apparent when using questionnaires (we discuss concrete issues related to ASC in the Different Motivation Constructs section). This is closely related to the Jingle-Jangle Fallacies discussed in Marsh et al. ( 2003 , p. 192). Third, questionnaires might not measure reliably motivation constructs that are not trait-like and subject to temporal and situational fluctuations (e.g., situational interest) (also see our discussion of this point in Time scale of the relations section above). In practice, self-reports cannot be sampled with high frequency during learning (see process-oriented measures below). Fourth, questionnaires are problematic from a developmental perspective because, across age groups, there might be varying factor structures in empirical data. Furthermore, some children may be too young to process some motivation constructs. Finally, self-reports are sensitive to demand characteristics and a tendency to give socially desirable answers (e.g. students who are familiar with the implicit theory of intelligence might tend to report that they endorse a growth mindset, Lüftenegger & Chen, 2017 ).

Most recent discussions of motivation-achievement interactions emphasize the need for alternative methods to self-report questionnaires. These alternatives include experience sampling, daily diaries, think-aloud protocols, observations, and structured interviews (Eccles & Wigfield, 2020 ). These alternatives have their strengths, but some limitations remain, such as the subjective nature of these measures and a possible high demand on research participants’ cognitive resources when a large number of measures are administered during a session. In addition, some demand frequent small breaks during a task to report internal states, which may interfere with the flow of the task.

Several alternative methods are available to observe and measure motivation or engagement “online” during learning, for example by using frequent choices of learners or video observations (Järvenoja et al., 2018 ). With the development of new technologies, it is now also possible to collect such data longitudinally on a large scale. For example, MathGarden, an online math learning tool, provides access to math learning data of thousands of students. Motivation is indexed by the frequency and length of voluntary, self-initiated practice, and can be linked to learning and performance (Hofman et al., 2018 ). Other promising process-oriented measures are eye-tracking and facial emotional expressions (D’Mello et al., 2008 ; Grafsgaard et al., 2014 , 2011 ; Nye et al., 2018 ; van Amelsvoort & Krahmer, 2009 ).

Another process-oriented approach uses physiology for high-frequency and non-interfering measures of motivational states. We will briefly discuss the use of autonomic nervous system (ANS) and central nervous system (CNS) measures. ANS techniques can be used to measure arousal , which is defined as higher activation of the sympathetic relative to the parasympathetic system. Motivated and effortful behavior is accompanied by increased arousal, and thus ANS techniques can provide an index of motivation. Popular techniques are electrodermal activity (EDA), electrocardiograms (ECG), and impedance cardiography (ICG). Sympathetic arousal measured with EDA has been associated with emotion, cognition, and attention (Critchley, 2002 ). Sympathetic arousal can also be measured with pre-ejection period (Tavakolian, 2016 )—which is the time in between “the electrical depolarization of the left ventricle and the beginning of the ventricular ejection” (Lanfranchi et al., 2017 , p. 145). One shared challenge with EDA and ECG is that arousal is a “fuzzy” construct, meaning many things, yet nothing specific (Mendes, 2016 ). A common factor that elicits EDA is subjective salience or motivational importance . Pre-ejection period is often used as an index for effort mobilization in studies investigating motivational intensity theory (Brehm & Self, 1989 ). Suppression of parasympathetic activity, which can be measured as reduction in high frequency heart rate variability, has been associated with effortful control (Spangler & Friedman, 2015 ) and emotion regulation (Beauchaine, 2015 ), but a recent meta-analysis supports a more general role in top-down self-regulation (Holzman & Bridgett, 2017 ).

A CNS measure of motivational states can be provided by electroencephalography (EEG). Higher mental effort/workload has been associated with attenuated parietal alpha activity (Brouwer et al., 2012 , 2014 ; Fink et al., 2005 ), higher frontal theta activity (Cavanagh & Frank, 2014 ; Klimesch, 2012 ), and a higher theta/alpha ratio. Another useful EEG index of motivation is asymmetrical frontal activity, which has been proposed to index motivational direction . Approach and avoidance motivation are respectively related to greater left and right frontal activity (Kelley et al., 2017 ).

It should be noted that none of these process-oriented measures has currently been established as reliable enough to replace verbal reports. A standard conclusion is that “more research is needed” (Holzman & Bridgett, 2017 ). A constructive way forward, which Fulmer and Frijters ( 2009 ) and Scheiter et al. ( 2020 ) strongly advocate, is to triangulate multiple methods, including self-reported and process-oriented measures. Given that physiological measures are relatively new, triangulation can help establish their reliability and validity. For example, EEG could be measured along with behavioral process-oriented task measures of effort. This allows testing whether fluctuations in theta and alpha activities are due to subjective effort mobilization and not due to other processes such as emotional arousal. Such triangulation studies can point the way to reliable online measures of motivation that do not rely exclusively on self-reports.

Measuring Achievement

While achievement is a less-fraught construct than motivation, it still presents its own challenges. First, achievement is nearly always bound to a specific domain, for example mathematics (Arens et al., 2017 ) or reading skill (Ehm et al., 2019 ; Sewasew & Koester, 2019 ). It is unclear whether findings generalize from one domain to others. It is possible that there are quantitative or even qualitative differences between domains in how motivation and achievement interact, for example as a function of the feeling of flow that is or is not associated with performance within the domain.

A second aspect of achievement that may affect results is the type of measurement used. Achievement can be measured using standardized tests and grades in schools (Arens et al., 2017 ; Marsh et al., 2016 ), but for example also through teacher or self-assessment (Chamorro-Premuzic et al., 2010 ). These tend to vary substantially in reliability and validity and yield different results (e.g., stronger reciprocity for school grades than for test scores; Marsh et al., 2016 ). Moreover, in longitudinal studies, it is often difficult to assess whether performance at different moments in time truly reflects the same skill. For example, studies of reading skill may assess basic letter decoding skills in a first wave, and complex reading comprehension in the last (Sewasew & Schroeders, 2019 ). Such changes in tested skills are likely to lead to a lower stability of scores, and skew estimates of change over time. This consideration would speak for designs (discussed above) with shorter periods between measurement waves, where the same measures can be used in different waves.

A third aspect of achievement which may be important is that achievement can be construed as mastery of skills, which usually grows over time, or as performance relative to peers, which by definition cannot grow for all students. Studies typically use raw test scores as a dependent measure to assess this (Huang, 2011 ; Scharmer, 2020 ), which reflect mastery of skills. What is communicated to students, on the other hand, tends to be performance relative to peers (e.g., rankings or grades, which tend to be age-normed either explicitly or implicitly). This implies that perceived performance (see Fig. 1 ) will be based on relative performance, and not on the absolute achievement that researchers tend to study.

Scope of the Theories and Generalizability of Findings

Studies investigating motivation-achievement interactions have often studied the development of these processes separately during childhood, adolescence, and early adulthood. It is therefore unclear whether results can be generalized across developmental stages. Furthermore, as in many subfields of psychology, the majority of research in this area has been conducted in Western, educated, industrialized, rich, and democratic (WEIRD) societies (Henrich et al., 2010 ), where, for example, rates of schooling are much higher than other places (e.g. the Global South). Here, we outline considerations of generalizability across developmental stages and ethnic and sociocultural settings.

Generalization Across Developmental Stages

Childhood and adolescent development is characterized by rather different trajectories for academic achievement (with a general pattern of improvement with age) than for academic motivation (with a general pattern of decrease during adolescence, as well as diversification in sources of motivation) (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ). As a result, we can speculate that the reciprocal relationships between motivation and achievement will change with age. Below, we first highlight findings on changes in motivation across development, and next describe the consequences of developmental differences on reciprocal relations between motivation and achievement, as a function of age, developmental, and academic stages (such as puberty or school grade).

The way in which value guides goal pursuit transforms profoundly from childhood to adolescence to adulthood (Davidow et al., 2018 ), and is reflected in changes in reward sensitivity and cognitive control. At the individual level, motivational beliefs related to competence, control and agency, intrinsic and extrinsic motivation, and subjective task value undergo significant changes throughout the lifespan (Wigfield et al., 1998 , 2019 ). Social cognitive accounts often postulate that the development of more sophisticated cognitive capacities with age allows adolescents to improve performance but also to be more aware of their own abilities and those of their peers (Dweck, 2000 , Scherrer and Preckel, 2019 ). As children go through school, previously held optimistic beliefs on competency become more realistic or even pessimistic (Fredricks & Eccles, 2002 ; Jacobs et al., 2002 ; Scherrer & Preckel, 2019 ; Watt, 2004 ). A meta-analysis by Scherrer and Preckel ( 2019 ) found a small but significant overall decrease in several motivation constructs including academic self-concept, intrinsic motivation, mastery, and performance-approach achievement goals over the course of elementary and secondary school. However, for several other constructs, including self-esteem, academic self-efficacy, and performance-avoidance achievement goals, there was no consistent developmental trend across empirical studies. Overall, this heterogeneity in developmental patterns of various motivation constructs suggests that the reciprocal interactions with achievement may also follow different trajectories across development and still need to be investigated.

Beyond the individual level, social influences on learning and motivation within the family, peer, and school contexts (see Fig. 1 ) also play a significant role in the changes in motivation and achievement (Nolen & Ward, 2008 ; Wigfield et al., 1998 ). Sensitivity to social context continues to develop through childhood and adolescence, transforming through the different school stages (Ladd et al., 2009 ). Broadly speaking, motivation for academic activities decreases between childhood and adolescence, and motivation reorients toward social and novel situations (Crone & Dahl, 2012 ). According to the stage-environment fit account, the decline in academic motivation in adolescents is driven by a mismatch between their newly developed needs and their social settings (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ). Specifically, the transition to middle and high schools is usually accompanied by changes in peer relationships, friendship, and teacher-student relationships, an increase in normative and performance-focused evaluation and a decrease in perceived autonomy. Adolescence is especially characterized by heightened social influences on motivation (Casey, 2015 ): social interactions become increasingly important and peer affiliation motivation peaks (Brown & Larson, 2009 ).

Indeed, peer relationships show a stronger influence on academic self-concept for seventh graders, compared to fifth graders (Molloy et al., 2011 ). As children transition into middle school, there is increased competition for grades and typically a larger pool of peers that serve as a reference group (Molloy et al., 2011 ). During adolescence, same-aged peers in school can motivate academic achievement to a larger extent, and a stronger focus on performance rather than mastery goals is sometimes empirically observed (Maehr & Zusho, 2009 , but see Scherrer et al., 2020 ; Scherrer & Preckel, 2019 where meta-analytic findings point to declines in both mastery and performance goals).

In sum, individual developmental changes in self-concept, self-regulation, social influence, and the values attributed to certain academic goals suggest that reciprocal motivation-achievement relations from one age group cannot be readily generalized to other ages (Marsh & Martin, 2011 ). Qualitative and quantitative differences in the reciprocal relationship between motivation and achievement thus seem plausible, but the lack of developmentally appropriate measures complicates comparisons across different stages (Fulmer & Frijters, 2009 ). Populations of different ages have distinct motivation factor structures (Rao & Sachs, 1999 ) and young children do not yet have the cognitive and memory capacity to process some motivation constructs and contextual references (Fulmer & Frijters, 2009 ).

Taken together, it is critical to understand how changes in motivation interact with changes in abilities, and affect behavior across different age groups and school career. The literature would greatly benefit from an integration of research across a broader age range, and identifying continuities and discontinuities in the reciprocal relationship between motivation and performance across development. One way to do this is to leverage accelerated longitudinal designs, with multiple measurements of cohorts with different starting ages and differentiation between multiple motivation constructs (Guay et al., 2003 ; Marsh & Martin, 2011 ; Scherrer & Preckel, 2019 ).

Generalization Across Sociocultural Settings

The reciprocal relationship between motivation and achievement may also take different shapes across contexts, as students belong to different ethnic, gender, socioeconomic (SES), and cultural groups. However, the majority of current research on the reciprocal relations between motivation and academic achievement has suffered from what can be considered a sampling bias problem (Pollet & Saxton, 2019 ), i.e., conducted using homogenous samples in terms of ethnicity (Marsh & Martin, 2011 ) and cultural background (Henrich et al., 2010 ). In the meta-analysis by Valentine et al. ( 2004 ), which showed that samples from non-Western countries tended to have larger effect sizes than those from Western countries, there were only four non-Western samples out of a total of 60 samples. In her meta-analysis of Scharmer ( 2020 ), 90% of samples were collected in WEIRD countries (Australia, USA, and Western Europe, with fully half using German samples). This is problematic, given that even within WEIRD samples, motivation of students from different groups (e.g., African Americans vs. European Americans) is influenced by different factors, and may contribute differently to their academic achievement (Cohen et al., 2009 ). Ten years later, the remark of Marsh and Martin ( 2011 ) thus still stands that it is premature to conclude that the reciprocal relationship between motivation and achievement is universal.

Demonstrating this across diverse populations is important for three reasons. Firstly, even the same motivation construct might contribute differently to achievement across groups. For example, Chiu and Klassen ( 2010 ), using PISA data and a very large and diverse sample ( N participant = 88,590, N country = 34), found a positive link between mathematics self-concept and mathematics achievement, but this relationship was moderated by cross-country differences in cultural orientations (specifically, degree of egalitarianism, rigidity in gender roles, aversion to uncertainty). As mentioned above, Sachisthal et al. ( 2019 ) also showed that across populations different motivation constructs are central in the network of constructs.

Second, it is not unlikely that different groups have diverging motivation constructs. For instance, general self-concept is conceptualized differently across cultures (Becker et al., 2012 ; Taras et al., 2010 ; Vignoles et al., 2016 ). Thus, the extent to which academic self-concept contributes to a general sense of self likely differs across groups (Hansford & Hattie, 1982 ). Chen and Wong ( 2015 ) also found that Chinese students assigned different meanings to performance-approach and performance-avoidance goals than what is usually found in Western populations. As a result, interventions may need to target different factors in different sociocultural settings.

Finally, there might be culture-dependent or population-specific pathways connecting the relationship between motivation and achievement. For example, culture is likely to have a strong influence on attributional processes (see extensive theoretical discussion in Graham, 2020 ; empirical data in Chiu & Klassen, 2010 ) and implicit theory of intelligence (W. W. Chen & Wong, 2015 ). Chiu and Klassen ( 2010 ) found that calibration of mathematics self-concept (i.e., the degree to which judgments of one’s competence in a domain accurately reflect actual performance) was positively related to mathematics achievement. However, this link was significantly stronger in places where the prevailing culture was more egalitarian or more tolerant of uncertainty.

Such findings suggest differences between sociocultural contexts are not just gradual but also likely to be qualitative. This would threaten the generalizability of findings (Henrich et al., 2010 ). Note that many of the empirical studies cited in this section are non-longitudinal. Reciprocal relationships between motivation and achievement may look different from what we currently know when representative samples are included. It is thus highly relevant for future motivation research to increase ethnic, and other group diversity in their studies. This can be done by better sampling within geographical boundaries (Pollet & Saxton, 2019 ) and by reaching out to under-researched territories such as in Africa, Middle East, Southeast Asia, Central Asia, and South America.

Diversifying study populations can be tough, but is essential for new understanding of human universals and specifics in motivation. For example, collecting experimental data across countries offers alternative perspectives to experimental set-ups and findings, which subsequently prompt researchers to rethink the constructs of interest and their operationalizations (Vu et al., 2017 ). Nevertheless, there are innovative solutions to overcome practical difficulties, including collaborating with researchers who reside in places where certain specificity and universality in motivation constructs can be expected (as outlined in some of the examples above) and making use of networks of researchers such as Psychological Science Accelerator to get access to multiple laboratories and populations across the world ( https://psysciacc.wordpress.com/ ).

Discussion and Conclusions

We have summarized theories of motivation and analyzed these specifically with regards to how they conceptualize reciprocal interactions between motivation and achievement. This led to a summary of pathways between motivation and achievement, depicted in Fig. 1 . The common denominator between theories suggested reciprocal positive influences of motivation on achievement and vice versa, which has been supported by much previous research. We reviewed the strengths of the underlying data, but also limitations and gaps in the evidence. This led to a research agenda consisting of the following recommendations for future studies on the relationship between motivation and performance: (1) include multiple motivation constructs (on top of ASC), (2) investigate behavioral mediators, (3) consider a network approach, (4) align frequency of measurement to expected change rate in intended constructs and include multiple time scales to better understand influences across time-scales, (5) check whether designs meet the criteria for measuring causal, reciprocal inferences, (6) choose an appropriate statistical model, (7) apply alternatives to self-reports, (8) consider various ways of measuring achievement, and (9) strive for generalization of the findings to various age, ethnic, and sociocultural groups.

One of the hardest problems to solve is the lack of studies that allow for firm causal inferences. Most studies contain sophisticated statistical analyses of longitudinal data. While impressive, the underlying data remains correlational in nature and susceptible to explanations in terms of the presence of a (time-varying or time-invariant) third variable (or variables) that is not accounted for in a model, yet does have a causal effect on the outcomes. Usami et al. ( 2019 ) outline three assumptions that need to be checked when making causality inferences in the context of longitudinal designs. These are the assumptions of consistency, of positivity after controlling for confounders, and of no unobserved confounders (see full the discussion in Usami et al., 2019 ). In our view, the trickiest is the third assumption: “the relation between x and y must not be explained by other causes”(Antonakis et al., 2010 , p. 1087; Usami et al., 2019 ). There seems to be no way to conclusively rule out the presence of such confounders. Substantial knowledge about potential confounders and their characteristics, and using that knowledge to select the most appropriate cross-lagged model, is necessary.

We argued that the strongest support for causal claims on motivation-achievement relations would be studies manipulating either motivation or achievement at one time point and studying the effects on motivation-achievement interactions across subsequent time points. Such studies do not yet exist to our knowledge. Many studies do show effects of manipulations affecting motivation thereby having an effect on achievement, but these studies have not looked at longitudinal interactions. The other pathway (i.e., achievement → motivation) has not been studied extensively, because of difficulties identifying manipulations that would directly affect achievement but not motivation.

A way to work around this problem is to manipulate perceived achievement, instead of true achievement (our lab study, manuscript in preparation). In this experiment, participants perform a learning task that lasts an hour. Their motivation and achievement are measured at multiple consecutive time points. Halfway through the experiment, a manipulation of perceived feedback is introduced: participants receive rigged feedback that their achievement has dropped to below peer average. The causal relations between motivation and achievement can be examined because manipulated perceived achievement leads to corresponding changes in motivational beliefs, to changes in motivational behaviors and eventually, to changes in actual achievement across multiple consecutive time points. Another example of manipulation of achievement can be found in Bejjani et al. ( 2019 ) where they used a feedback manipulation (a competence-threatening IQ score) to study its effect on subsequent motivation and learning.

Furthermore, we have argued that motivation can best be seen as a constellation of highly related, multidimensional constructs, and manipulations of motivation may directly or indirectly influence achievement and vice versa. An innovative method to study the motivation-achievement relationship can be a network approach, where observational and interventional data are used to estimate a causal graph. The idea is that to estimate causal relations, one variable can be manipulated at a time, and its effects on other variables can be observed. The network approach is also beneficial in the classroom context where there are many variables to take into account which cannot be independently manipulated (Yeager & Walton, 2011 ).

Our discussion of various theories of motivation in education showed how densely motivation and performance are interlinked. They can best be seen as a cycle of mutually reinforcing relations. While a cycle suggests a closed loop, we list several options for outside intervention, which are represented by the gray arrows in Fig. 1 . Some of these are well-researched practical interventions, such as autonomy support and training in helpful attributions (Hulleman et al., 2010 ). Others are excellent avenues for future research. For example, designing how feedback reaches the learner offers opportunities for motivation support. Research has shown how to provide negative feedback in a way that does not lower a learner’s motivation (Fong et al., 2019 ), how peer comparison can be harnessed for motivation (Mumm & Mutlu, 2011 ), or how feedback can be given without giving away that errors have been made (Narciss & Huth, 2006 ). It is our impression that this research has so far not reached all classrooms.

In conclusion, this view of a cycle between motivation and achievement, as shown in Fig. 1 , has intuitive appeal and fits well with theories of academic motivation. However, empirical evidence for a cycle is far from complete. The research agenda we have presented contains important challenges for future research aimed at elucidating how motivation and achievement exactly interact, and whether a cycle and a network of constructs are good ways of conceptualizing these interactions. As academic motivation typically drops considerably in adolescence and may be lower for some groups (e.g., through the effects of SES, stereotype threat, and the likes), such evidence is necessary for gaining knowledge on how to best intervene in the cycle, and bring out the best in each student.

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Acknowledgements

We would like to thank Sibel Altikulaç, Abe Hofman, and Simone Plak who participated in our Expert Workshop in Motivation-Performance Cycle in Math Learning in Amsterdam in June 2019 where we discussed ideas for this paper. We would like to thank Milene Bonte, Wouter van den Bos, Camila Bosano, and Bruce McCandliss who are members of the advisory board for the Jacobs Foundation project of which the subproject to write this manuscript is a part. We also want to thank Asmar Isilak who helped with the first database search for empirical studies on the motivation-achievement cycles in learning .

This work was supported by the Jacobs Foundation Science of Learning pilot grant to Nienke van Atteveldt and Brenda R. J. Jansen [project number 2019 1329 00]. Nienke van Atteveldt was also supported by a Starting Grant from the European Research Council (ERC, grant #716736). The funders had no role in study design, decision to publish, or preparation of the manuscript.

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Vu, T., Magis-Weinberg, L., Jansen, B.R.J. et al. Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda. Educ Psychol Rev 34 , 39–71 (2022). https://doi.org/10.1007/s10648-021-09616-7

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The Emerging Neuroscience of Intrinsic Motivation: A New Frontier in Self-Determination Research

Stefano i. di domenico.

1 Institute for Positive Psychology and Education, Australian Catholic University, Strathfield, NSW, Australia

Richard M. Ryan

2 Department of Clinical and Social Sciences in Psychology, University of Rochester, Rochester, NY, USA

Intrinsic motivation refers to people’s spontaneous tendencies to be curious and interested, to seek out challenges and to exercise and develop their skills and knowledge, even in the absence of operationally separable rewards. Over the past four decades, experimental and field research guided by self-determination theory (SDT; Ryan and Deci, 2017 ) has found intrinsic motivation to predict enhanced learning, performance, creativity, optimal development and psychological wellness. Only recently, however, have studies begun to examine the neurobiological substrates of intrinsic motivation. In the present article, we trace the history of intrinsic motivation research, compare and contrast intrinsic motivation to closely related topics (flow, curiosity, trait plasticity), link intrinsic motivation to key findings in the comparative affective neurosciences, and review burgeoning neuroscience research on intrinsic motivation. We review converging evidence suggesting that intrinsically motivated exploratory and mastery behaviors are phylogenetically ancient tendencies that are subserved by dopaminergic systems. Studies also suggest that intrinsic motivation is associated with patterns of activity across large-scale neural networks, namely, those that support salience detection, attentional control and self-referential cognition. We suggest novel research directions and offer recommendations for the application of neuroscience methods in the study of intrinsic motivation.

Intrinsic motivation refers to the spontaneous tendency “to seek out novelty and challenges, to extend and exercise one’s capacity, to explore, and to learn” (Ryan and Deci, 2000 , p.70). When intrinsically motivated, people engage in an activity because they find it interesting and inherently satisfying. By contrast, when extrinsically motivated, people engage in an activity to obtain some instrumentally separable consequence, such as the attainment of a reward, the avoidance of a punishment, or the achievement of some valued outcome. Early evidence for the distinction between these types of motivation came from experimental studies demonstrating that tangible rewards can undermine intrinsic motivation (Deci, 1971 ). That is, contrary to the ideas that intrinsic and extrinsic motivation are additive or synergistically positive (e.g., Atkinson, 1964 ; Porter and Lawler, 1968 ), studies show that people experience less interest and exhibit less spontaneous engagement with activities for which they were initially intrinsically motivated after receiving tangible rewards for performing the activities (Deci et al., 1999 ).

Self-determination theory (SDT; Ryan and Deci, 2000 , 2017 ) has emerged as the principle framework for the study of intrinsic motivation. Intrinsic motivation is frequently assessed behaviorally in terms of freely pursued activities, and experientially through self-report questionnaires that probe the reasons for one’s engagement with activities, as well as specific affective states such as interest, curiosity and fun. Intrinsic motivation has also been assessed in the laboratory through the coding of specific exploratory and manipulatory behaviors and facial displays of interested engagement (Reeve and Nix, 1997 ). Since the earliest demonstrations of the undermining effect, many experimental and field studies have found intrinsic motivation to be associated with enhanced learning, performance, creativity, and affective experience. Further, a large body of research within SDT has examined the situational factors (e.g., types of rewards, feedback, communication styles) that undermine or facilitate the expression of intrinsic motivation (Ryan and Deci, 2017 ). These studies have made it clear that although intrinsic motivation is a lifelong psychological growth function, by no means is its expression automatic; rather, intrinsic motivation depends on ambient supports for basic psychological needs, especially those for competence (feeling effective) and autonomy (feeling volitional).

Despite being a longstanding topic within the field of motivation, only recently have researchers begun to use neuroscience methods to examine intrinsic motivational processes (Ryan and Di Domenico, 2016 ). The use of neuroscience methods is an important new frontier for intrinsic motivation research for at least three interrelated reasons. First, to state the obvious, experience and behavior are mediated by the brain and a complete account of intrinsic motivation therefore requires an understanding of the neural systems that support it. Second, neuroscience affords the examination of internal processes that are not accessible by self-reports of experience or behavioral observations. A neuroscience of intrinsic motivation therefore promises new insights that introspective and behavioral methods alone cannot afford. Finally, neuroscience methods can be used to investigate motivational processes at a higher level of resolution than experiential and behavioral methods. Neuroscience methods therefore have the potential to refine conceptual accounts of intrinsic motivation by articulating the granular processes that comprise it. In a relevant discussion, Ochsner ( 2007 ; p.51) stated that, “The combination of multiple streams of data allows researchers to converge on theoretical explanations that are robust and flexible and are not tied to a single specific experimental methodology”. Intrinsic motivation would seem to be an especially ripe topic for neuroscience precisely because of the large body of empirical data that has already been garnered at the experiential and behavioral levels of analysis.

Our purpose of this review article is to survey the progress of neuroscience research on intrinsic motivation. Because intrinsic motivation is not a uniquely human capacity (Harlow, 1953 ; Wilson, 2000 ; Ryan and Deci, 2017 ) we review conceptual developments in the comparative affective neurosciences (Panksepp, 1998 ; Panksepp and Biven, 2012 ) that inform the concept of intrinsic motivation. Such considerations are essential for appreciating intrinsic motivation as a basic organismic capacity and for helping to clarify its unique components in humans (Ryan and Di Domenico, 2016 ). Building upon these insights, we map the phenomenology of intrinsic motivation onto the neural substrates of motivational processes that are encompassed by intrinsic motivation. Against the backdrop of these preliminary ideas, we then review recent studies that have examined the neural correlates of intrinsic motivation. To anticipate our main conclusions, affective neuroscience suggests that human intrinsic motivation is based in ancient mammalian systems that govern exploration and play. Neuroimaging studies, which have up to now focused on curiosity and mastery tendencies, indicate that intrinsically motivated states are subserved by neural regions that are central to dopamine systems. These studies also hint at the possible role of dynamic switching between large-scale brain networks involved in salience detection, attentional control and self-referential cognition. On the basis of these ideas, we suggest novel research directions and offer recommendations for the application of neuroscience methods in the study of intrinsic motivation.

Intrinsic Motivation: An Organismic Growth Process

Long before Deci’s ( 1971 ) experiments concerning intrinsic motivation in humans and its undermining by rewards, Harlow ( 1950 ) documented this effect in rhesus monkeys. He coined the term intrinsic motivation to describe his observation that these primates would persist in playing with mechanical puzzles even in the absence of external rewards. Indeed, he observed that the introduction of rewards for playing led these primates to decrease their spontaneous manipulative explorations, relative to those not exposed to external rewards. These and related observations of spontaneous exploratory and play behaviors defied some behaviorist views that intentional behaviors are invariably controlled by reinforcement contingencies within the environment (e.g., Skinner, 1953 ).

Early work with both primates and rats also exposed some limitations of empirical drive theory (Hull, 1943 ), which asserted that motivated behaviors aim to reduce internal drives that stem from physiological need deficits. Because intrinsic motivation often ensues in the absence and, on occasion, independent of such deprivations, it was poorly explained by traditional drive reduction accounts (White, 1959 ). Early attempts to amend drive theory led researchers to postulate the existence of various exploratory drives as the basis for seemingly spontaneous curiosity, exploratory and manipulatory behaviors (e.g., Butler, 1953 ; Harlow, 1953 ; Montgomery, 1954 ; Myers and Miller, 1954 ). Apart from its lack of parsimony, this “drive-naming” approach could not be reconciled with the observations that exploratory activities do not resemble consummatory responses and that animals often behave to increase rather than decrease such exploratory drives (White, 1959 ; Deci and Ryan, 1985 ). As we shall see, these points are respectively echoed in the contemporary research on the role of dopamine in motivation, particularly by Berridge ( 2007 ) distinction between incentive “wanting” and consummatory “liking” and by Panksepp’s ( 1998 ) work on the mammalian SEEKING system.

Variants of psychodynamic drive theory (Freud, 1927/1960 ) proved similarly inadequate. For example, Fenichel ( 1945 ) proposed that exploratory and mastery behaviors are driven by the desire to reduce anxiety in the face of novel stimuli. A revision of this hypothesis may be approximated from the perspective of Gray and McNaughton’s ( 2000 ) septo-hippocampal theory of anxiety. An extensive program of research has established that novel stimuli—on an a priori basis—represent potential sources of both punishment and reward, elicit tendencies for both avoidance and approach, and therefore often arouse anxious uncertainty and prompt cautious investigatory behaviors. The investigatory behaviors instigated by the septo-hippocampal system include risk assessment and scanning the environment and one’s memory to resolve the motivational conflict, that is, to compute whether approach or avoidance should predominate.

Intrinsically motivated curiosity, exploration and mastery behaviors, however, pertain to specific types of novel stimuli, namely, those that present optimal challenges or optimal inconsistencies with one’s extant knowledge and that accordingly energize tendencies to approach (White, 1959 ; Csikszentmihalyi, 1990 ; Loewenstein, 1994 ; Ryan and Deci, 2017 ). Consistent with the work of Gray and McNaughton ( 2000 ) intrinsic motivation researchers have long noted that whereas too much novelty relative to a person’s skill and knowledge produces anxiety, too little novelty produces to boredom. During intrinsic motivation, feelings of interest and positive excitement predominate over both anxiety and boredom. Indeed, such exploratory states entail searching for novelties and challenges and, moreover, acting on the world to elicit novelties and to discover new problems (Harlow, 1953 ; White, 1959 ; Deci and Ryan, 1985 ). These observations indicate that intrinsically motivated exploratory and mastery behaviors are primarily energized by interest and appetitive mastery tendencies, not anxiety reduction.

Given the shortcomings of operant behaviorism and drive theory in regards to intrinsic motivation, White ( 1959 ) proposed effectance motivation as a general behavioral and developmental propensity of many organisms. Seemingly prescient of later developments in the affective neurosciences (e.g., Panksepp, 1998 ; Panksepp and Biven, 2012 ), White ( 1959 ) argued that effectance motivation is inherent to the activity of the central nervous system and described it as “what the neuromuscular system wants to do when it is otherwise unoccupied (e.g., by strong homeostatic drives) or is gently stimulated by the environment” (p.321). According to White ( 1959 ), the satisfactions associated with the effectance motive are not tied to consummatory activities, but are instead intrinsic to the arousal and maintenance of the activities that stem from it. Along similar lines, DeCharms ( 1968 ) proposed that intrinsic motivation is based in people’s “primary propensity” to experience themselves as causal agents, that is, to experience their own actions as having an internal perceived locus of causality . DeCharms’s ( 1968 ) insightful theorizing helped set the stage for the earliest experiments on the undermining effect as it suggested that external enticements and pressures that detract one from experiencing oneself as the center of initiation of their own behaviors—that undermine autonomy—can diminish intrinsic motivation (Deci and Ryan, 1985 ).

By the mid-1980s, numerous studies had examined the effects of various situational factors on the expression of intrinsic motivation (Deci and Ryan, 1985 ). This research indicated that events like the provision of positive feedback (e.g., Fisher, 1978 ; Boggiano and Ruble, 1979 ; Ryan, 1982 ) and choice (e.g., Zuckerman et al., 1978 ) enhanced intrinsic motivation and that negative feedback (e.g., Deci and Cascio, 1972 ; Vallerand and Reid, 1984 ), deadlines (e.g., Amabile et al., 1976 ), and other external impositions (e.g., surveillance; Lepper and Greene, 1975 ) generally diminished intrinsic motivation. To account for the diversity of findings from these and other studies, Deci and Ryan ( 1985 ), drawing on the ideas of White ( 1959 ) and DeCharms ( 1968 ), proposed that intrinsic motivation is a lifelong psychological growth function that is based in the basic psychological needs for competence and autonomy. Competence refers to feelings of effectance, the sense of growing mastery in activities that are optimally challenging and that further develop one’s capacities. Autonomy refers to an experience of volition and integrity, the sense that one’s behavior is authentic and self-organized rather than internally conflicted and pressured or externally coerced. Within SDT, competence and autonomy are seen as essential elements in people’s active propensities to seek out challenges, to be curious and interested, and to develop and express their capacities: when these needs are supported, intrinsic motivation may ensue; when these needs are thwarted, intrinsic motivation is undermined (Ryan and Deci, 2017 ).

In terms of both evolution and development, intrinsic motivation confers many adaptive consequences for organisms (Ryan and Deci, 2017 ). For example, intrinsic motivation exposes organisms to novel situations and therefore occasions the development of diverse skills and competencies to cope with uncertain future situations. Intrinsic motivations are particularly important for those species that have a protracted period of postnatal development and occupy complex habitats (Wilson, 2000 ). In this vein, Deci and Ryan ( 2000 , p.252) pointed out that:

If people did not experience satisfaction from learning for its own sake (but instead needed to be prompted by external reinforcements) they would be less likely to engage the domain-specific skills and capacities they inherited, to develop new potentialities for adaptive employment, or both … for instance, by aiding in the discovery of alternative food sources, mapping the complexities of game migrations, or taking interest in skills, rituals, and social rules transmitted by other group members.

Extending this evolutionary thinking, Ryan and Hawley ( 2016 ) reviewed empirical evidence that competence and autonomy satisfactions supply proximal supports for intrinsically motivated activities even when the adaptive consequences of such activities are not the phenomenal aims of the individuals enacting them.

At the level of personality functioning, intrinsic motivation provides the impetus for individuals to learn about particular subject areas and to differentiate their interests, fostering the development of personal identities that confer a sense of authenticity, meaning, and purpose (Deci and Ryan, 1985 ; Ryan and Deci, 2012 ). For example, meta-analyses and field studies point to intrinsic motivation as perhaps the most important form of motivation in school achievement (e.g., Taylor et al., 2014 ; Froiland and Worrell, 2016 ). In a related vein, Peterson ( 1999 ) argued that the dedicated and courageous pursuit of one’s interests optimizes personality development by incrementally exposing one to new ideas and challenges, thereby preventing ideological rigidity and fostering learning, growth, and meaning in life. Indeed, various scholars have proposed that intrinsically motivated self-examination plays a key role in the development of the highest human virtues, including wisdom (e.g., Habermas, 1972 ; Csikszentmihalyi and Rathmunde, 1990 ; Vervaeke and Ferraro, 2013 ).

Neuroethological Perspectives on Mammalian Exploration: A Starting Point for Conceptualizing Intrinsic Motivation in the Brain

We previously pointed out (Ryan and Di Domenico, 2016 ) that the concept of intrinsically motivated exploration is consistent with the “affective neuroethological” perspective of Panksepp and colleagues (Panksepp, 1998 ; Ikemoto and Panksepp, 1999 ; Alcaro et al., 2007 ; Alcaro and Panksepp, 2011 ; Panksepp and Biven, 2012 ). These researchers have argued that mammals are hardwired with a general-purpose SEEKING system that energizes many types of foraging and exploratory activities. Although the SEEKING system does service homeostatic imbalances and is responsible for energizing learned appetitive behaviors, it continuously operates to keep animals in a state of exploratory engagement with their environments. That is, the SEEKING system is believed to function as an objectless appetitive system—a “goad without a goal”—until the exploratory disposition it produces leads to the discovery and learning of useful regularities.

The SEEKING system is a spontaneous, unconditioned behavior generator that takes animals to places, actively and inquisitively, where associated learning mechanisms allow them to develop knowledge structures, to guide their foremost evolutionary action tools (inbuilt emotional systems) to create more structures—more higher mental processes—which facilitate survival (Panksepp and Biven, 2012 , p.135).

It is worth pointing out that even within radical behaviorism this inherent activity of organisms could not be fully ignored, though it was marginalized by Skinner’s concept of “the operant”. Skinner acknowledged that organisms do “operate” on their environments, but regarded such exploratory activities as random behaviors that come under the control of external reinforcement.

The core structures that comprise the SEEKING system in the rat are the ventral tegmental area (VTA), the nucleus accumbens (NAcc), the ventromedial prefrontal cortex (VMPFC), and the dopaminergic projections originating from the VTA that innervate these areas (Panksepp, 1998 ; Panksepp and Biven, 2012 ). These regions are frequently called the “brain reward network” because, as Olds and Milner ( 1954 ) discovered, rats will learn to instrumentally obtain electrical stimulations in this area. However, the invigorated searching and sniffing following such electrical stimulations look like states of invigorated curious exploration rather than states of calm satiation (see White, 1959 ): “The most dramatic observation…is that animals getting this kind of brain stimulation frantically explore their environments, taking notice of all the new stimuli they encounter” (Panksepp and Biven, 2012 , p.126). These basic SEEKING urges are elaborated into more complex forms of exploration in behaviorally and cognitively sophisticated animals: our dexterity affords the manipulation and exploration of complex objects and our cognitive faculties afford interest in ideas, abstract objects and possibilities that we can explore and manipulate with our minds. The SEEKING system is thus believed to energize “many mental complexities that humans experience as persistent feelings of interest, curiosity, sensation seeking and, in the presence of a sufficiently complex cortex, the search for higher meaning” (Panksepp, 1998 , p.145).

The first experimental studies on intrinsic motivation were conducted on nonhuman animals (Harlow, 1950 ) and it is therefore fitting that the first insights on the neurobiology of intrinsic motivation have been derived in animal research. Although generalizations based on animal research must be made with caution, affective neuroscience suggests that human intrinsic motivation is an elaboration of ancient mammalian motivations for exploratory SEEKING. The affective neuroethological point of view from which this system is conceptualized dovetails the organismic perspective from which SDT developed (Ryan and Deci, 2017 ). It is remarkable and telling that independent lines of research stemming from such methodologically diverse traditions should converge on similar points of view.

Related Perspectives on Intrinsic Motivation

Aspects of intrinsic motivation have also been examined from perspectives other than SDT. Because some of the empirical studies that we review in upcoming sections are based on these related topics, we briefly summarize these perspectives here to note similarities and differences with SDT. We also briefly review topics that bear important conceptual relations to intrinsic motivation and note the utility of these for helping to inform the emerging neuroscience of intrinsic motivation.

The close relation between SDT’s concept of intrinsic motivation and Csikszentmihalyi ( 1990 ) concept of flow has been noted for a long time (Deci and Ryan, 1985 , 2000 ). Flow refers to experiential states of total absorption, optimal challenge, and non-self-conscious enjoyment of an activity. Like intrinsic motivation, when people experience flow, the satisfactions they experience are inherent to the activity itself and their behavior is “autotelic” ( auto = self, telos = goal) or performed for its own sake. Like SDT, flow theory emphasizes the phenomenology of intrinsic motivation. Flow theory is particularly articulate in its description of the optimal challenges and ensuing competence satisfactions associated with intrinsic motivation. For example, Nakamura and Csikszentmihalyi ( 2014 ; p.90) describe the flow state as the subjective experience of engaging “just-manageable challenges by tackling a series of goals, continuously processing feedback about progress, and adjusting action based on this feedback”. However, apart from recognizing the autotelic (i.e., intrinsically motivating) aspects of flow activities, flow theory does not formally recognize autonomy as an essential component of flow (Deci and Ryan, 2000 ).

Loewenstein ( 1994 ) proposed an “information-gap” hypothesis of curiosity according to which curiosity arises when people experience a discrepancy between what they know and what they want to know. Although this knowledge discrepancy is supposedly experienced as aversive, satisfying curiosity is pleasurable and people therefore voluntarily seek to elicit curiosity. There are some obvious links between SDT and Loewenstein’s ( 1994 ) information-gap hypothesis of curiosity. First, feelings of curiosity are regularly referenced in descriptions of intrinsic motivation within SDT and Loewenstein ( 1994 ; p.87) correspondingly described curiosity as “an intrinsically motivated desire for specific information”. Second, both intrinsic motivation and curiosity seeking are processes that describe types of self-directed learning. Finally, although Lowenstein’s theory does not formally include the concept of autonomy, his notion of what constitutes an “information-gap” is well-aligned with SDT’s notion of competence. Specifically, one way to conceptualize information-gaps in knowledge is in terms of optimal incongruities between one’s extant knowledge structures and the unknown (Deci and Ryan, 1985 ). Intrinsically motivated activities, activities that are energized by the need for competence and that entail orienting toward novel stimuli and optimal challenges, can thus be seen as a process of continually seeking and reducing information-gaps in knowledge.

Perhaps the most notable divergence between SDT and Loewenstein’s account concerns his description of curiosity as a consummatory, drive-reduction process—i.e., the closure of information gaps. A close variant of this discrepancy between organismic and drive-theory accounts of intrinsic motivation was resolved in the earliest critiques of the drive-naming approach to intrinsically motivated exploration. Both White ( 1959 ) and Deci and Ryan ( 1985 ) pointed out that while curiosity for particular objects or places may satiate the tendency to explore those particular objects or areas, the tendency to explore itself is not satiated. Thus, SDT’s organismic account of intrinsic motivation and Loewenstein’s ( 1994 ) drive-reduction account of curiosity seeking can be reconciled by recognizing that curiosity is a more delimited phenomenon subsumed by intrinsically motivated exploration. Piaget ( 1971 ), in his organismic account of cognitive development, expressed a similar view. He proposed that cognitive-behavioral schemata possess inherent functions to assimilate new information and to elaborate pre-existing skills, inherent functions that can be productively described as being intrinsically motivated (Ryan and Deci, 2017 ). Piaget ( 1971 ) thus saw curiosity as a continual process that “goes through various steps, in the sense that whenever one problem is solved, new problems are opened up. These are new avenues for curiosity” (Evans, 1973 , pp.68–69).

A concept related to intrinsic motivation has also emerged within the “Five-Factor” or “Big Five” model of personality research (John et al., 2008 ; McCrae and Costa, 2008 ). Specifically, DeYoung ( 2010 , 2013 ) has argued that the higher-order trait plasticity (i.e., the shared variance of extraversion and openness/intellect) represents stable interindividual differences in people’s exploratory tendencies. Apart from the obvious difference that intrinsic motivation refers to a motivational state , whereas plasticity refers to dispositional trait , these two phenomena have some notable features in common. Like intrinsic motivation, plasticity entails being “actively engaged with the possibilities of the environment, both generating and attending to novel aspects of experience” (DeYoung, 2010 , p.27, and although plastic exploration has not been formally described using the concept of autonomy, people high in plasticity are hypothesized to “desire exploration for its own sake (i.e., they treat it as a goal in itself) and engage in it even at times when exploration will not obviously further their goals” (DeYoung, 2013 , p.8). These conceptual links between plasticity and intrinsic motivation are important because recent years have seen a marked increase in the field’s understanding of the neurobiology of plasticity, most specifically, its association with dopamine (DeYoung, 2013 ). These insights inform some of the ideas in the current presentation.

Mapping Phenomenology to Brain Function: Toward a Neurobiological Model of Human of Intrinsic Motivation

The biggest challenge facing researchers who wish to examine the neural substrates of intrinsic motivation is the absence of an overarching neurobiological framework with which to derive and test specific hypotheses. Exploratory studies, though potentially useful for advancing research in novel directions when conducted with suitably large samples, typically afford lower statistical power and are therefore prone to both Type I errors (false positives) and Type II errors (false negatives). This limitation of exploratory research is especially problematic in neuroimaging studies that do not specify a priori regions of interest and need to correct for multiple statistical tests when comparing neural activity across multiple brain regions (Allen and DeYoung, 2016 ). In the absence of a guiding theory, it is also difficult to design experimental paradigms that are optimally suited to examine specific components of intrinsic motivation.

Recognizing that even a preliminary neurobiological account of intrinsic motivation could facilitate theory-driven research and provide a useful vantage point for aligning the disparate empirical studies to date, we offer an initial iteration by mapping the phenomenology of intrinsic motivation to the neural substrates of motivational processes that are encompassed by intrinsic motivation. We organize these ideas in the form of summary propositions. Against the backdrop of these propositions, we review studies that have examined the neural correlates of intrinsic motivation.

Proposition I: Intrinsic Motivation is Supported by Dopaminergic Systems

Intrinsic motivation is a complex cognitive, affective, and behavioral phenomenon that is likely mediated by multiple neural structures and processes. For this reason, a useful point of entry for elucidating the neurobiology of intrinsic motivation is to consider the broad neurotransmitter systems that may underlie it.

Three lines of evidence suggest that dopamine is a key substrate of intrinsic motivation. First, as the review above suggests, intrinsic motivation in humans is an elaboration of the exploratory activities subserved by the mammalian SEEKING system, and dopamine is central to the neurochemistry of this system (Panksepp, 1998 ; Panksepp and Biven, 2012 ). Second, like intrinsic motivation, dopamine is associated with increased positive affect, cognitive flexibility, creativity (Ashby et al., 1999 ), behavioral persistence (Salamone and Correa, 2016 ), and exploration in the face of novelty (DeYoung, 2013 ). Importantly, the positively affective states associated with dopamine reflect energized appetitive “wanting” not consummatory “liking”, the hedonic effects of which are mediated by opioids (Berridge, 2007 ; Kringelbach and Berridge, 2016 ). Third, there is some evidence of a direct link between intrinsic motivation and dopamine. Using positron emission tomography, de Manzano et al. ( 2013 ) found that people who are disposed to experience intrinsically motivated flow states in their daily activities have greater dopamine D2-receptor availability in striatal regions, particularly the putamen. This finding suggests that people’s capacities for intrinsic motivation are associated with the number of targets within the striatum for dopamine to act upon. More recently, Gyurkovics et al. ( 2016 ) found that carriers of a genetic polymorphism that affects striatal D2-receptor availability were more prone to experience flow during study- and work-related activities. Altogether, it would thus seem reasonable to forward the initial working hypothesis that dopamine is a key substrate of intrinsic motivation.

Dopamine neurons originate in the midbrain and have two modes of activity, tonic and phasic (Grace, 1991 ). In the tonic mode, the neurons exhibit a steady baseline rate of firing in which dopamine is steadily released to target structures. This tonic activity promotes the normal functioning of relevant neural circuits (Schultz, 2007 ) and may reflect the general strength of animals’ exploratory SEEKING tendencies (Alcaro and Panksepp, 2011 ). In the phasic mode, dopamine neurons exhibit short bursts of activity or inactivity (above or below their baseline) in response to specific events, resulting in an increase or decrease of dopamine in target structures lasting several seconds. The phasic mode of dopamine transmission may “transiently activate SEEKING patterns in coincidence with specific cue- or context-dependent information, attributing to such information an incentive motivational, action-orienting effect” (Alcaro and Panksepp, 2011 , p.1810). Of course, the tonic and phasic modes of dopamine transmission likely interact in complex ways to regulate intrinsic motivation. For example, Alcaro et al. ( 2007 ) advanced the hypothesis that moderately high levels of tonic dopamine optimize the SEEKING behavior promoted by phasic dopamine release: when tonic levels of dopamine are too low, phasic signals lack the efficacy to promote exploration; but when tonic levels are too high, phasic signals lose their informational value and exploratory behavior patterns are uncoupled from relevant contextual stimuli. Given the nascent state of the field, however, questions about how the tonic and phasic modes of dopamine release interact to influence intrinsic motivation remain outside the scope of the present effort. We instead focus on making the less specific case for a general relation between dopamine and intrinsic motivation.

Bromberg-Martin et al. ( 2010 ) recently proposed a model of dopaminergic function that is based on the recognition of two types of dopamine neurons that exhibit distinct types of phasic activity: value-coding neurons and salience-coding neurons. We review the properties of these neurons and their relevance to intrinsic motivation below.

Value-coding neurons are phasically excited by unexpected rewarding events and inhibited by unexpected aversive events; events that are wholly expected elicit little or no response. Value-coding dopamine neurons are found in the ventromedial substantia nigra pars compacta (SN) and throughout the VTA. From these midbrain regions, these neurons project axons that innervate the NAcc shell, the dorsal striatum (caudate and putamen), and the VMPFC, where they send signals about the availability of rewards, evaluation of outcomes, and learning. The phasic signals emitted by value-coding neurons are classically recognized as “reward-prediction errors” within neobehaviorist theories and are believed to be an important mechanism through which animals learn about external reinforcement contingencies (Schultz, 2007 ).

However, Tricomi and DePasque ( 2016 ) recently argued that, even in the absence of external rewards, this dopaminergic pathway registers the endogenous signals of positive and negative feedback that are elicited during the performance of many activities. The types of activities that people find intrinsically motivating provide just-manageable challenges, clear proximal goals, and immediate feedback (Nakamura and Csikszentmihalyi, 2014 ; Ryan and Deci, 2017 ). For example:

As people work on crossword puzzles, they get feedback from the task itself (i.e., the letters fit), and they are likely to feel a sense of joy from making progress at puzzles that challenge them…No external feedback is required, and, surely, the task-inherent positive feedback is gratifying and helps sustain interest and persistence (Ryan and Deci, 2017 , p.154).

Another way of describing this optimally challenging nature of intrinsically motivated activities is to say that the positive and negative feedback that people receive during their performance of such activities is not entirely unexpected—a performative context that suggests phasic dopaminergic signaling. Following Tricomi and DePasque ( 2016 ), we therefore propose that a high rate of dopaminergic signaling within the value system is inherent to the performance of intrinsically motivating activities.

In addition to value-coding neurons, Bromberg-Martin et al. ( 2010 ) identified salience-coding neurons. These dopamine neurons are phasically excited by both unexpected rewarding and punishing events. These neurons are found in the dorsolateral SN and medial VTA, and project to the NAcc core, the dorsal striatum, and the dorsolateral PFC (DLPFC). The regions innervated by salience-coding neurons support the orienting of attention, cognitive processing, and the invigoration of actions. Dovetailing Loewenstein’s ( 1994 ) information-gap hypothesis of curiosity, DeYoung ( 2013 ) proposed that salience-related dopaminergic activity energizes exploration “in response to the incentive value of the possibility of gaining information—that is, it drives curiosity and the desire for information” (p.4). Curiosity and interest are of course long recognized components of intrinsic motivation. For example, the Intrinsic Motivation Inventory (Ryan et al., 1983 ), a self-report measure of intrinsic motivation for experimental settings that is used to predict free choice behavioral persistence, includes items such as “I found the task very interesting” and “I thought the task was very boring (reverse scored)”. These items describe the type of eager attentiveness and behavioral engagement that may be associated dopaminergic salience signaling. Thus, building on DeYoung ( 2013 ), we propose that the salience-coding system also subserves intrinsic motivation.

Apart from the aforementioned studies by de Manzano et al. ( 2013 ) and Gyurkovics et al. ( 2016 ), empirical studies have not directly examined the link between dopamine and intrinsic motivation. However, if intrinsic motivation is associated with dopaminergic transmission, then intrinsically motivated activities should be associated with activation across core regions of the dopaminergic systems identified by Bromberg-Martin et al. ( 2010 ). In the paragraphs that follow, we focus on neuroimaging findings relating intrinsic motivation to activity within regions of the dopaminergic value system. Studies relating intrinsic motivation to activity within regions of the dopaminergic salience system are reviewed separately because such findings are also consistent with the complementary proposition that intrinsic motivation is associated with patterns of activity across specific large-scale neural networks.

Murayama et al. ( 2010 ) examined the neural correlates of the undermining effect using fMRI. University undergraduates were asked to play a game-like stopwatch task in which they were asked to press a button within 50 ms of the 5 s mark. In a series of pilot tests, the authors determined that students found this task challenging and interesting, and therefore suitable for examining intrinsic motivation. Like classic studies on the undermining effect (e.g., Deci, 1971 ), participants were divided in two groups: a reward group that received performance-contingent rewards for each successful trail and a control group that received no payments. During an initial scanning session, participants in both groups evidenced greater activity in the midbrain and caudate upon the receipt of success feedback relative to failure feedback. Subsequent to the experimental manipulation, and consistent with previous behavioral studies on the undermining effect, participants in the reward group were less likely to voluntarily engage with the task during a free-choice time period relative to those in the control group. Importantly, this behavioral undermining of intrinsic motivation was paralleled by reduced activity in the caudate and midbrain during a second scanning session when monetary rewards were no longer administered to the reward group. In contrast the unrewarded group maintained its previous levels of activation. This difference in activity between the control and experimental groups is consistent with the idea that the dopaminergic value system is responsive to cues that signal task-related progress during intrinsically motivated activities.

In a more recent fMRI study, Murayama et al. ( 2015 ) had participants perform an adapted version of the stopwatch task (Murayama et al., 2010 ) in two conditions: an autonomy condition in which they were free to choose the appearance of the stopwatch according to their preferences and a forced-choice condition in which they had to proceed with the stopwatch selected by the computer. Results indicated that activity within the VMPFC (bilateral gyrus rectus and medial orbitofrontal gyrus) was greater upon the receipt of success feedback than failure feedback. However, this effect was modulated by the type of the trial conditions. On the one hand, the VMPFC exhibited similarly high levels of activity across success and failure feedback after free-choice (autonomy) trials. On the other hand, this region exhibited marked reductions in activity after forced-choice trials. Importantly, this sustained activity within the VMPFC in response to failure feedback was associated with enhanced performance within the free-choice condition. Present evidence suggests that value coding dopamine neurons in the midbrain project to the VMPFC and that this structure is involved in learning from negative reward prediction errors and updating outcome expectations during learning (Bromberg-Martin et al., 2010 ). These results are thus consistent with the idea that intrinsic motivation, and the perceived autonomy that phenomenally supports it, is associated with activity within the dopaminergic value system.

Conceptually related to these fMRI studies is research examining intrinsic motivation using electroencephalography (EEG). Two specific EEG waveforms that have been associated with intrinsic motivation are the “error-related negativity” (ERN) and the “feedback-related negativity” (FRN). Both of these waveforms are negative-going deflections in EEG recordings that arise during speeded-response tasks. Whereas the ERN appears within 100 ms following the commission of errors, the FRN appears between 200 ms and 350 ms following the receipt of negative feedback. Holroyd and Coles ( 2002 ) proposed that both the ERN and FRN arise as a consequence of phasic reductions in midbrain dopaminergic signaling to ACC, the purported neural generator of these waveforms. These phasic reductions in dopamine transmission to the ACC, and the consequent ERN and FRN, are believed to constitute a learning signal that tunes the ACC to optimize behavioral performance, an account that parallels the reward-prediction signaling of value-coding dopamine neurons (Schultz, 2007 ; Bromberg-Martin et al., 2010 ).

In a sample of school children, Fisher et al. ( 2009 ) found that intrinsic academic motivation was associated with larger ERN amplitudes during a flanker task. In a study that paralleled the design of Murayama et al. ( 2010 ), Ma et al. ( 2014 ) found that participants who had received performance-contingent monetary rewards while performing a challenging activity evidenced reduced FRN amplitudes whereas those in a control group evidenced consistently pronounced FRNs. In another study, this time paralleling the design of Meng and Ma ( 2015 ) and Murayama et al. ( 2015 ) found that having the opportunity to exercise choice during the performance of an intrinsically motivating task was associated with larger FRN amplitudes. An important caveat to these studies is their small sample sizes ( N = 17, 36 and 18, respectively), which raises uncertainty about reliability of their reported effects. To this point, Jin et al. ( 2015 ; N = 16) found lower FRN amplitudes when participants received negative feedback on a supposedly interesting task relative to a boring task. In light of these small sample sizes and diversity of findings, it is clear that more decisive larger-sample studies are required. Nevertheless, the available evidence from these EEG studies is generally consistent with the idea that intrinsic motivation is associated with dopaminergic signaling.

Other evidence of a link between intrinsic motivation and the dopaminergic system comes from studies examining the neural correlates of curiosity. Kang et al. ( 2009 ; Study 1) used fMRI to examine curiosity as it is framed by the information-gap theory of Loewenstein ( 1994 ). We previously pointed out that the information-gaps in people’s knowledge structures, and the ensuing feelings of curiosity that such gaps elicit, can be productively framed in terms of people’s orienting toward optimal challenges. Participants reflected upon a series of trivia questions (e.g., What instrument was invented to sound like a human singing?) and rated their curiosity for each one. During the presentation of the trivia questions, items that elicited greater curiosity were associated with activations in the left caudate and parahippocampal gyri (PHG). Furthermore, when trivia answers were revealed following incorrect responses, participants’ level of curiosity was associated with greater activity in the midbrain and left PHG. Although Bromberg-Martin et al. ( 2010 ) did not identify the PHG as a core component of the dopamine system, Kang et al. ( 2009 ) point out that this region is involved in successful memory encoding and its activity during states of curiosity is therefore consistent with the idea that intrinsic motivation is associated with enhanced learning.

A follow-up study by Gruber et al. ( 2014 ) more directly assessed the relation between curiosity and learning. This study used trivia questions similar to Kang et al. ( 2009 ) to examine if states of curiosity improved memory for task-relevant information and for information that was incidental to the main task. Incidental information consisted of face stimuli that were presented to participants when they anticipated trivia answers. During the presentation of trivia questions, curiosity was associated with activity in the left SN/VTA, bilateral NAcc, and bilateral dorsal striatum. Furthermore, replicating the behavioral results of Kang et al. ( 2009 ; Study 2), in both immediate and delayed memory tests, participants recalled more answers for high- relative to low-curiosity questions. Extending these previous behavioral findings, Gruber et al. ( 2014 ) also found enhanced recall of incidental face stimuli presented during high-curiosity questions. These memory effects were associated with greater activity in the SN/VTA and the hippocampus during the presentation of trivia questions and increased functional connectivity between these regions when participants anticipated answers to the trivia questions.

Proposition II: Intrinsic Motivation Entails Dynamic Switching between Brain Networks for Salience Detection, Attentional Control and Self-Referential Cognition

A complementary approach to theorizing about the neural systems that support intrinsic motivation is to map its phenomenology with the activity of large-scale neural networks (Ryan and Di Domenico, 2016 ). Research on structural and functional brain organization has revealed multiple large-scale brain networks that support various cognitive functions (Bressler and Menon, 2010 ). Among these is the so-called salience network , which is believed to support the detection of subjectively important events and the mobilization of attentional and working memory resources in the service of goal-directed behavior (Menon and Uddin, 2010 ; Menon, 2015 ). The salience network is anchored in the anterior insula (AI) and dorsal ACC and includes major subcortical nodes in the amygdala, NAcc, the SN, and VTA. These subcortical nodes are believed to send signals about the motivational significance of stimuli to the AI; the AI in turn is believed to integrate this information with incoming sensory inputs from both the external environment and the viscera for the bottom-up detection of contextually important events. Through its reciprocal connections with the dACC, a key structure for executive control, the AI is believed to selectively amplify neural signals of important events for the effective deployment of cognitive resources.

Little is presently known about the specific role of dopamine in the functioning of the salience network. However, AI does receive inputs from the amygdala, the likely source of the motivational salience signals sent to dopamine neurons in the midbrain, from the ventral striatum, which receives dopaminergic projections from the midbrain, and from the SN and the VTA, the midbrain regions from which dopamine neurons originate (Bromberg-Martin et al., 2010 ; Menon and Uddin, 2010 ; Menon, 2015 ). Additionally, the AI has reciprocal connections with the dACC, which likely receives direct input from both value- and salience-coding dopamine neurons (Bromberg-Martin et al., 2010 ). These connections imply that the AI may play a role in contextualizing the signals of motivational significance transmitted by both value- and salience-coding dopamine neurons. Most relevant in this regard is the suggestion that the AI functions as a dynamic hub for modulating the activity of two other large-scale brain networks (Menon and Uddin, 2010 ; Menon, 2015 ). The first, known as the default mode network , has major nodes in the MPFC and the posterior cingulate cortex (PCC). These regions show high levels of activity during passive resting states (Gusnard and Raichle, 2001 ), tasks involving internally-focused, self-referential cognition (Northoff et al., 2006 ), and mind-wandering (Mason et al., 2007 ). The second, known as the central executive network , includes the DLPFC and the posterior parietal cortex (PPC). The regions of this network, which are important substrates of working memory and executive functions, typically show elevated activity during cognitively demanding, externally-focused tasks. Importantly, activity across the default mode and central executive networks often fluctuates in an antagonistic manner, such that activity in one is often accompanied by suppressed activity in the other.

The antagonistic dynamic between the default mode and central executive networks, along with the role of the salience-mediating switching instigated by the AI, may inform three characteristics of intrinsic motivation. First, in its most experientially abundant state, intrinsic motivation entails cognitive absorption and non-self-conscious enjoyment of an activity (Csikszentmihalyi, 1990 ; Nakamura and Csikszentmihalyi, 2014 ). This phenomenology suggests diminished activity within regions of the default mode network, which are commonly activated during self-focused mental activity (e.g., self-reflection, rumination) and mind-wandering, and heightened activity within the central executive network, which is engaged during bouts of externally focused attention. Second, intrinsic motivation is reliably associated with enhanced performance, cognitive flexibility, and deeper conceptual learning (e.g., Grolnick and Ryan, 1987 ). This relation between intrinsic motivation and enhanced task performance is consistent with, and may be partly explained by, greater mobilization of the central executive network during intrinsically motivating tasks (Ryan and Di Domenico, 2016 ). Third, classic perspectives that describe autonomy or authenticity as a state of “organismic congruence” (e.g., Rogers, 1961 ) characterize it as an embodied cognitive process whereby sensory and visceral information is permitted to access and direct one’s attention, in a bottom-up manner, to events of subjective importance and meaning (also see Peterson, 1999 ). The salience network, and the AI most specifically, with its receipt of sensory and visceral input and its interoceptive functions (Craig, 2009 ; Menon and Uddin, 2010 ; Menon, 2015 ), would seem well-suited to support this aspect of autonomy, especially during intrinsic motivation when people orient themselves to stimuli that spontaneously grip their attention and interest.

Neuroimaging studies have reported patterns of neural activity consistent with the idea that intrinsic motivation recruits the salience and central executive networks, while suppressing the default mode network. In the aforementioned study by Murayama et al. ( 2010 ), the undermining of intrinsic motivation was associated with decreases in lateral PFC activity in response to task onset cues. The study by Murayama et al. ( 2015 ) found increased activity within the midbrain, ACC, and bilateral insula in response to free-choice (autonomy) cues relative to forced-choice cues at the onset of task trials. The curiosity studies by Kang et al. ( 2009 ) and Gruber et al. ( 2014 ) found greater activity within the lateral PFC during curiosity-inducing questions. More recently, Marsden et al. ( 2015 ) observed neural activations within several structures that comprise the SN. Specifically, their study found participants who spent more free-choice time solving remote-associate word problems (i.e., a behavioral marker of intrinsic motivation) showed greater activity in the ACC, amygdala, anterior and posterior insula, PHG, and caudate nucleus after trial onsets that immediately followed negative feedback for preceding trials. Jepma et al. ( 2012 ) examined the neural correlates of perceptual curiosity. Participants viewed blurry images of otherwise easily recognizable objects that induced feelings of curiosity, and were subsequently shown clear images of the objects to satisfy their curiosity. Results indicated that induction of curiosity was associated with significant activations within the AI and ACC, the core regions of the salience network, and significant deactivations within regions associated with the default mode network. Additionally, this study found that the resolution of perceptual curiosity was associated with activity within the left caudate, putamen, and NAcc, regions that comprise the core of the dopaminergic system.

A set of studies (Lee et al., 2012 ; Lee and Reeve, 2013 ) examined the neural correlates of intrinsic motivation by comparing patterns of neural activity when undergraduate students imagined themselves performing intrinsically motivating activities (e.g., “writing an enjoyable article”) and extrinsically motivating activities (e.g., “writing an extra-credit article”). Most prominently, these studies found preferential activity within insular regions when participants imagined the enactment of intrinsically motivating activities. Building on this initial work, Lee ( 2016 ) more recently described the results of an fMRI study that examined functional connectivity between striatal regions and the AI when participants attempted trivia questions and anagrams. Results indicated that when participants worked on intrinsically motivating problems (curiosity inducing-questions and competence-enabling anagrams) they evidenced greater activity and functional connectivity between these regions.

Klasen et al. ( 2012 ) examined the neural correlates of flow using fMRI recordings obtained during free play of a video game. The authors developed an objective coding system for examining different components of the flow experience based on player-generated video game contents. Consistent with the idea that intrinsic motivation is associated with dopaminergic signaling, optimal challenge was associated greater activity within the caudate, putamen, and NAcc. Consistent with the idea that intrinsic motivation is associated with suppressed activity in default mode regions, concentrated focus and goal clarity were associated with reduced activity within the orbitofrontal cortex and ACC. Additionally, task-related failure was associated with increased activity within the cuneus, a structure included within the default mode network.

In another fMRI study, Ulrich et al. ( 2014 ) examined the neural correlates of flow by asking participants to work on mental arithmetic task and comparing experimentally challenging levels with boredom and overload conditions. Results indicated that flow states were associated with increased activity in the left putamen and left IFG, again implicating core regions of both the dopaminergic system and the central executive network. Results also indicated that flow was associated with deactivations within the MPFC, suggesting suppressed default mode network activity. In another study, Yoshida et al. ( 2014 ) used functional near-infrared spectroscopy (fNIRS) to examine the time course of neural activations within the prefrontal cortex during states of flow and boredom when participants played Tetris ® . Again, consistent with the idea that intrinsically motivated states recruit central executive regions, results indicated increasing bilateral activity within lateral PFC regions during flow. However, a subsequent fNIRS study by Harmat et al. ( 2015 ) that compared prefrontal activity across easy, optimally challenging, and difficult levels of Tetris did not any differences. Despite these mixed findings, the results of existing studies altogether suggest that future research would benefit by explicitly testing the proposition that intrinsic motivation is associated with patterns of activity across the salience, central executive, and default mode networks.

Recent years have witnessed an emerging interest in the neurobiological systems that support intrinsic motivational processes. Although this area of inquiry is young, conceptual and empirical evidence points to the role of dopaminergic systems in supporting intrinsically motivated behaviors. Across different mammalian species, there appear to be linkages between dopamine and the positive experiences associated with exploration, new learning and interest in one’s environment (Panksepp, 1998 ; Panksepp and Biven, 2012 ). Building on Bromberg-Martin et al.’s ( 2010 ) distinction between dopaminergic value- and salience-coding and on previous work respectively mapping these systems onto the phenomenology of competence (Tricomi and DePasque, 2016 ) and interest (DeYoung, 2013 ), we propose that intrinsic motivation entails both types dopaminergic transmission. Because these dopamine systems entail distinct neural structures, future neuroimaging studies have a strong conceptual basis for specifying distinct a priori regions of interest. Beyond that, evidence suggests that intrinsic motivation involves alterations between the neural networks of salience detection, attentional control, and self-referential cognition (Menon and Uddin, 2010 ; Menon, 2015 ). Better understanding of these large-scale neural dynamics may provide greater resolution of the processes that support high quality learning and performance.

Despite the clear conceptual relationship between intrinsic motivation and dopaminergic transmission, only two existing studies provide direct evidence of an association between these two processes (de Manzano et al., 2013 ; Gyurkovics et al., 2016 ). The bulk of existing research provides indirect support to the hypothesis that dopamine is a substrate of intrinsic motivation in that the core regions innervated by dopamine neurons are activated during intrinsic motivation. Pharmacological manipulations of dopamine thus represent an important new research direction. Indeed, such manipulations have already been fruitfully applied in the study of dispositional traits (e.g., Wacker and Smillie, 2015 ) and their application in the study of motivational states would seem a natural extension. Pharmacological manipulations of dopamine may, for example, allow researchers to more precisely decode the neural mechanisms that mediate the undermining effect of externally contingent rewards on intrinsic motivation.

The link between dopaminergic systems and intrinsic motivation may also prove useful for developmental roboticists for whom the topic of intrinsic motivation has recently fallen into purview (e.g., Gottlieb et al., 2013 , 2016 ). The stated goal of developmental robotics is to design embodied agents that self-organize their development by constructing sensorimotor, cognitive, and social skills over the course of their interactions with the environment. Roboticists have proposed that in order for embodied agents to be capable of intrinsic motivation, they must not only be outfitted with computational systems that orient them toward novel, surprising, or uncertain stimuli, but also with meta-monitoring processes that track their learning progress in their investigation of such stimuli (Gottlieb et al., 2013 , 2016 ). Without meta-monitoring processes that track learning, agents will likely get trapped investigating stimuli that are random or otherwise unlearnable, precluding the possibility for self-directed development. The existence of salience- and value-coding dopaminergic systems, respectively capable of tracking novelty and rewarding feedback, may partially represent an organic instantiation of the type of computational system that Gottlieb et al. ( 2013 , 2016 ) hypothesize to be a requirement for intrinsic motivation. We believe that roboticists are well-positioned to discover the types of computational problems that need to be solved for a full understanding of the neural substrates of intrinsic motivation. We thus hope that some of the present ideas will help to spur robotics research on intrinsic motivation.

Future studies are also needed to directly test the hypothesis that intrinsically motivated states entail dynamic switching between the salience, central executive and default mode networks. Beyond traditional fMRI analyses comparing activity in a priori regions across intrinsically and non-intrinsically motivated states, this hypothesis specifically encourages the use of connectivity analyses and the adoption of chronometric techniques that can provide information about the dynamics and directionality of activity across large-scale networks (e.g., Sridharan et al., 2008 ). This research direction may help to not only elucidate the neural basis of intrinsic motivation but also to identify the neural mechanisms through which intrinsic motivation enhances learning and performance outcomes, especially on tasks that require depth of processing and high-quality engagement.

Beyond Exploration, Curiosity and Mastery: Intrinsically Motivated Social Play

SDT uses intrinsic motivation as a broad term for diversity of activities that are inherently rewarding and growth promoting (Ryan and Deci, 2017 ). This is a large class of behaviors, minimally including curious exploration and mastery tendencies, on the one hand, and social play, on the other (Ryan and Di Domenico, 2016 ). To date, human neuroscience studies have focused on intrinsic motivation associated with curious exploration and mastery, rather than social play, and we accordingly based our review on this subset of intrinsically motivated behaviors. Yet, comparative affective neuroscience suggests that exploration and social play have both distinct and overlapping neurobiological and phenomenological underpinnings, the former being subserved by the SEEKING system and the latter by the PLAY system (Panksepp, 1998 ; Panksepp and Biven, 2012 ). The subcortical PLAY system governs the rough-and-tumble (R&T) interactions of mammals, energizing them to develop and refine their physical, emotional, and social competencies in a safe context (Panksepp, 1998 ; Pellis and Pellis, 2007 ; Trezza et al., 2010 ; Panksepp and Biven, 2012 ). In early mammalian development, R&T play constitutes a type of embodied social cognition that provides a basis for cooperation and the adaptive self-regulation of aggression (Peterson and Flanders, 2005 ). Humans are of course also capable of more sophisticated forms of play beyond R&T such as common playground games, sports play and friendly humor, but such human play may be nonetheless organized around basic PLAY motivations (Panksepp, 1998 ; Panksepp and Biven, 2012 ).

We might therefore regard play as intrinsically motivated socialization (Ryan and Di Domenico, 2016 ), an expression of people’s complementary tendencies toward autonomy and sociality in development (Ryan, 1995 ; Ryan et al., 1997 ). Indeed, research in SDT suggests that in addition to competence and autonomy, people have a basic psychological need for relatedness , the sense of feeling meaningfully connected with others (Ryan and Deci, 2017 ). Whereas strong associations between exploratory intrinsic motivations and satisfactions of competence and autonomy have been clearly demonstrated, relatedness is usually seen to play a more distal role in the expression of these intrinsic motivations. Specifically, relatedness satisfactions provide people (especially children) with a sense of safety, a secure base from which their exploratory tendencies can be more robustly expressed (Ryan and Deci, 2017 ). Recognition of social PLAY signifies the centrality of the need for relatedness in some intrinsically motivated activities.

Interest in the overlaps and contrasts between intrinsically motivated exploration and play is thus an important agenda for future studies and both are relevant to intrinsic motivation as it is studied within SDT (Ryan and Di Domenico, 2016 ). Behavioral models of human intrinsic motivation have generally conflated exploration and play because these activities share common features such as an internal perceived locus of causality and perceived competence or mastery. Indeed, functional distinctions between intrinsically motivated exploration and object or manipulative play are subtle and suggest that, for many activities recognized as “playful”, the conflation is appropriate and productive. For example, Wilson ( 2000 ) suggested that “In passing from exploration to play, the animal or child changes its emphasis from ‘What does this object do?’ to ‘What can I do with this object?”’ (p.165). In fact, intrinsically motivated object play, manipulative play, and solitary gaming likely arise from the activity of the SEEKING system (Panksepp, 1998 ; Panksepp and Biven, 2012 ). Clearly, more empirical work is needed to differentiate these types of intrinsic motivation in humans.

Methodological Suggestions

Our principal intent in this review article, is to stimulate increasing integration between social behavioral research on intrinsic motivation and the neuroscience of motivation. We see many new and promising pathways opening up. At the same time, methodological issues persist that warrant serious considerations. We list but a few of these.

First, intrinsic motivation and the associated undermining effect of rewards on these behaviors pertain only to tasks that are interesting and enjoyable in the first place. Thus, researchers should pilot test target activities to ensure that the activities are suitable for examining the undermining effect. This is especially important in neuroscience, where contemporary methods such as fMRI often involve procedures that limit how interesting experimental tasks can be. Researchers should also use multi-method assessments of intrinsic motivation to validate their measures and to ensure that the correct behavioral phenomena are being tapped. For example, in an attempted (and failed) replication and extension of Murayama et al.’s ( 2010 ) fMRI study on the undermining effect, Albrecht et al.’s ( 2014 ) utilized a picture-discrimination task for which participants may not have been intrinsically motivated in the first place (pilot testing was not reported) and for which free-choice behavior was not examined as a dependent variable. In the absence of these important design characteristics, it is difficult to draw decisive conclusions from their experiment. Incidentally, we note that Albrecht et al.’s ( 2014 ) study did show that competence feedback increased participants’ self-reported fun and that it was also associated with increased activations within the midbrain, striatum, and lateral PFC, findings that are consistent with the idea that competence is associated with dopamine-related activity.

Second, replicability is a central concern, as it is throughout the social and personality neurosciences (Allen and DeYoung, 2016 ). Most studies to date have been small-sample investigations, and larger samples are needed if we are to derive foundational conclusions. A priori hypotheses concerning regions of interest will also add confidence to the interpretation of findings. Toward that end, the present review ought to provide future studies with a useful reference for making clear predictions about the neural basis of intrinsic motivation.

Intrinsic motivation is a topic of interest within both basic behavioral science and applied translational studies and interventions (Ryan and Deci, 2000 , 2017 ). Yet important to the progress of empirical research on intrinsic motivation is integrating what is known from phenomenological and behavioral studies with neuroscience studies. As we suggested at the outset, neuroscience holds potential for testing existing models of the situational and social determinants of intrinsic motivation as well as for providing greater resolution on the affective and cognitive processes that underpin such activities. Movement toward consilience is a central concern to SDT and our hope is that the current synthesis provides some broad stoke encouragement for that agenda.

Author Contributions

SID conceptualized and wrote the manuscript. RMR assisted in conceptualizing and writing the manuscript.

SID was supported in this research by a postdoctoral fellowship from the Social Sciences and Humanities Research Council of Canada.

Conflict of Interest Statement

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.

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  • 14.4 Recent Research on Motivation Theories
  • Introduction
  • 1.1 What Do Managers Do?
  • 1.2 The Roles Managers Play
  • 1.3 Major Characteristics of the Manager's Job
  • Summary of Learning Outcomes
  • Chapter Review Questions
  • Management Skills Application Exercises
  • Managerial Decision Exercises
  • Critical Thinking Case
  • 2.1 Overview of Managerial Decision-Making
  • 2.2 How the Brain Processes Information to Make Decisions: Reflective and Reactive Systems
  • 2.3 Programmed and Nonprogrammed Decisions
  • 2.4 Barriers to Effective Decision-Making
  • 2.5 Improving the Quality of Decision-Making
  • 2.6 Group Decision-Making
  • 3.1 The Early Origins of Management
  • 3.2 The Italian Renaissance
  • 3.3 The Industrial Revolution
  • 3.4 Taylor-Made Management
  • 3.5 Administrative and Bureaucratic Management
  • 3.6 Human Relations Movement
  • 3.7 Contingency and System Management
  • 4.1 The Organization's External Environment
  • 4.2 External Environments and Industries
  • 4.3 Organizational Designs and Structures
  • 4.4 The Internal Organization and External Environments
  • 4.5 Corporate Cultures
  • 4.6 Organizing for Change in the 21st Century
  • 5.1 Ethics and Business Ethics Defined
  • 5.2 Dimensions of Ethics: The Individual Level
  • 5.3 Ethical Principles and Responsible Decision-Making
  • 5.4 Leadership: Ethics at the Organizational Level
  • 5.5 Ethics, Corporate Culture, and Compliance
  • 5.6 Corporate Social Responsibility (CSR)
  • 5.7 Ethics around the Globe
  • 5.8 Emerging Trends in Ethics, CSR, and Compliance
  • 6.1 Importance of International Management
  • 6.2 Hofstede's Cultural Framework
  • 6.3 The GLOBE Framework
  • 6.4 Cultural Stereotyping and Social Institutions
  • 6.5 Cross-Cultural Assignments
  • 6.6 Strategies for Expanding Globally
  • 6.7 The Necessity of Global Markets
  • 7.1 Entrepreneurship
  • 7.2 Characteristics of Successful Entrepreneurs
  • 7.3 Small Business
  • 7.4 Start Your Own Business
  • 7.5 Managing a Small Business
  • 7.6 The Large Impact of Small Business
  • 7.7 The Small Business Administration
  • 7.8 Trends in Entrepreneurship and Small-Business Ownership
  • 8.1 Gaining Advantages by Understanding the Competitive Environment
  • 8.2 Using SWOT for Strategic Analysis
  • 8.3 A Firm's External Macro Environment: PESTEL
  • 8.4 A Firm's Micro Environment: Porter's Five Forces
  • 8.5 The Internal Environment
  • 8.6 Competition, Strategy, and Competitive Advantage
  • 8.7 Strategic Positioning
  • 9.1 Strategic Management
  • 9.2 Firm Vision and Mission
  • 9.3 The Role of Strategic Analysis in Formulating a Strategy
  • 9.4 Strategic Objectives and Levels of Strategy
  • 9.5 Planning Firm Actions to Implement Strategies
  • 9.6 Measuring and Evaluating Strategic Performance
  • 10.1 Organizational Structures and Design
  • 10.2 Organizational Change
  • 10.3 Managing Change
  • 11.1 An Introduction to Human Resource Management
  • 11.2 Human Resource Management and Compliance
  • 11.3 Performance Management
  • 11.4 Influencing Employee Performance and Motivation
  • 11.5 Building an Organization for the Future
  • 11.6 Talent Development and Succession Planning
  • 12.1 An Introduction to Workplace Diversity
  • 12.2 Diversity and the Workforce
  • 12.3 Diversity and Its Impact on Companies
  • 12.4 Challenges of Diversity
  • 12.5 Key Diversity Theories
  • 12.6 Benefits and Challenges of Workplace Diversity
  • 12.7 Recommendations for Managing Diversity
  • 13.1 The Nature of Leadership
  • 13.2 The Leadership Process
  • 13.3 Leader Emergence
  • 13.4 The Trait Approach to Leadership
  • 13.5 Behavioral Approaches to Leadership
  • 13.6 Situational (Contingency) Approaches to Leadership
  • 13.7 Substitutes for and Neutralizers of Leadership
  • 13.8 Transformational, Visionary, and Charismatic Leadership
  • 13.9 Leadership Needs in the 21st Century
  • 14.1 Motivation: Direction and Intensity
  • 14.2 Content Theories of Motivation
  • 14.3 Process Theories of Motivation
  • 15.1 Teamwork in the Workplace
  • 15.2 Team Development Over Time
  • 15.3 Things to Consider When Managing Teams
  • 15.4 Opportunities and Challenges to Team Building
  • 15.5 Team Diversity
  • 15.6 Multicultural Teams
  • 16.1 The Process of Managerial Communication
  • 16.2 Types of Communications in Organizations
  • 16.3 Factors Affecting Communications and the Roles of Managers
  • 16.4 Managerial Communication and Corporate Reputation
  • 16.5 The Major Channels of Management Communication Are Talking, Listening, Reading, and Writing
  • 17.1 Is Planning Important
  • 17.2 The Planning Process
  • 17.3 Types of Plans
  • 17.4 Goals or Outcome Statements
  • 17.5 Formal Organizational Planning in Practice
  • 17.6 Employees' Responses to Planning
  • 17.7 Management by Objectives: A Planning and Control Technique
  • 17.8 The Control- and Involvement-Oriented Approaches to Planning and Controlling
  • 18.1 MTI—Its Importance Now and In the Future
  • 18.2 Developing Technology and Innovation
  • 18.3 External Sources of Technology and Innovation
  • 18.4 Internal Sources of Technology and Innovation
  • 18.5 Management Entrepreneurship Skills for Technology and Innovation
  • 18.6 Skills Needed for MTI
  • 18.7 Managing Now for Future Technology and Innovation
  • Describe the modern advancements in the study of human motivation.

Employee motivation continues to be a major focus in organizational behavior. 35 We briefly summarize current motivation research here.

Content Theories

There is some interest in testing content theories (including Herzberg’s two-factor theory), especially in international research. Need theories are still generally supported, with most people identifying such workplace factors as recognition, advancement, and opportunities to learn as the chief motivators for them. This is consistent with need satisfaction theories. However, most of this research does not include actual measures of employee performance. Thus, questions remain about whether the factors that employees say motivate them to perform actually do.

Operant Conditioning Theory

There is considerable interest in operant conditioning theory, especially within the context of what has been called organizational behavior modification. Oddly enough, there has not been much research using operant conditioning theory in designing reward systems, even though there are obvious applications. Instead, much of the recent research on operant conditioning focuses on punishment and extinction. These studies seek to determine how to use punishment appropriately. Recent results still confirm that punishment should be used sparingly, should be used only after extinction does not work, and should not be excessive or destructive.

Equity Theory

Equity theory continues to receive strong research support. The major criticism of equity theory, that the inputs and outcomes people use to evaluate equity are ill-defined, still holds. Because each person defines inputs and outcomes, researchers are not in a position to know them all. Nevertheless, for the major inputs (performance) and outcomes (pay), the theory is a strong one. Major applications of equity theory in recent years incorporate and extend the theory into the area called organizational justice. When employees receive rewards (or punishments), they evaluate them in terms of their fairness (as discussed earlier). This is distributive justice. Employees also assess rewards in terms of how fair the processes used to distribute them are. This is procedural justice. Thus during organizational downsizing, when employees lose their jobs, people ask whether the loss of work is fair (distributive justice). But they also assess the fairness of the process used to decide who is laid off (procedural justice). For example, layoffs based on seniority may be perceived as more fair than layoffs based on supervisors’ opinions.

Goal Theory

It remains true that difficult, specific goals result in better performance than easy and vague goals, assuming they are accepted. Recent research highlights the positive effects of performance feedback and goal commitment in the goal-setting process. Monetary incentives enhance motivation when they are tied to goal achievement, by increasing the level of goal commitment. There are negative sides to goal theory as well. If goals conflict, employees may sacrifice performance on important job duties. For example, if both quantitative and qualitative goals are set for performance, employees may emphasize quantity because this goal achievement is more visible.

Expectancy Theory

The original formulation of expectancy theory specifies that the motivational force for choosing a level of effort is a function of the multiplication of expectancies and valences. Recent research demonstrates that the individual components predict performance just as well, without being multiplied. This does not diminish the value of expectancy theory. Recent research also suggests that high performance results not only when the valence is high, but also when employees set difficult goals for themselves.

One last comment on motivation: As the world of work changes, so will the methods organizations use to motivate employees. New rewards—time off instead of bonuses; stock options; on-site gyms, cleaners, and dental services; opportunities to telecommute; and others—will need to be created in order to motivate employees in the future. One useful path that modern researchers can undertake is to analyze the previous studies and aggregate the findings into more conclusive understanding of the topic through meta-analysis studies. 36

Catching the Entrepreneurial Spirit

Entrepreneurs and motivation.

Motivation can be difficult to elicit in employees. So what drives entrepreneurs, who by definition have to motivate themselves as well as others? While everyone from Greek philosophers to football coaches warn about undirected passion, a lack of passion will likely kill any start-up. An argument could be made that motivation is simply part of the discipline, or the outcome of remaining fixed on a purpose to mentally remind yourself of why you get up in the morning.

Working from her home in Egypt, at age 30 Yasmine El-Mehairy launched Supermama.me, a start-up aimed at providing information to mothers throughout the Arab world. When the company began, El-Mehairy worked full time at her day job and 60 hours a week after that getting the site established. She left her full-time job to manage the site full time in January 2011, and the site went live that October. El-Mehairy is motivated to keep moving forward, saying that if she stops, she might not get going again (Knowledge @ Wharton 2012).

For El-Mehairy, the motivation didn’t come from a desire to work for a big company or travel the world and secure a master’s degree from abroad. She had already done that. Rather, she said she was motivated to “do something that is useful and I want to do something on my own” (Knowledge @ Wharton 2012 n.p.).

Lauren Lipcon, who founded a company called Injury Funds Now, attributes her ability to stay motivated to three factors: purpose, giving back, and having fun outside of work. Lipcon believes that most entrepreneurs are not motivated by money, but by a sense of purpose. Personally, she left a job with Arthur Andersen to begin her own firm out of a desire to help people. She also thinks it is important for people to give back to their communities because the change the entrepreneur sees in the community loops back, increasing motivation and making the business more successful. Lipcon believes that having a life outside of work helps keep the entrepreneur motivated. She particularly advocates for physical activity, which not only helps the body physically, but also helps keep the mind sharp and able to focus (Rashid 2017).

But do all entrepreneurs agree on what motivates them? A July 17, 2017 survey on the hearpreneur blog site asked 23 different entrepreneurs what motivated them. Seven of the 23 referred to some sense of purpose in what they were doing as a motivating factor, with one response stressing the importance of discovering one’s “personal why.” Of the remaining entrepreneurs, answers varied from keeping a positive attitude (three responses) and finding external sources (three responses) to meditation and prayer (two responses). One entrepreneur said his greatest motivator was fear: the fear of being in the same place financially one year in the future “causes me to take action and also alleviates my fear of risk” (Hear from Entrepreneurs 2017 n.p.). Only one of the 23 actually cited money and material success as a motivating factor to keep working.

However it is described, entrepreneurs seem to agree that passion and determination are key factors that carry them through the grind of the day-to-day.

Hear from Entrepreneurs. 2017. “23 Entrepreneurs Explain Their Motivation or if ‘Motivation is Garbage.’” https://hear.ceoblognation.com/2017/07/17/23-entrepreneurs-explain-motivation-motivation-garbage/

Knowledge @ Wharton. 2012. “The Super-motivated Entrepreneur Behind Egypt’s SuperMama.” http://knowledge.wharton.upenn.edu/article/the-super-motivated-entrepreneur-behind-egypts-supermama/

Rashid, Brian. 2017. “How This Entrepreneur Sustains High Levels of Energy and Motivation.” Forbes. https://www.forbes.com/sites/brianrashid/2017/05/26/how-this-entrepreneur-sustains-high-levels-of-energy-and-motivation/2/#2a8ec5591111

  • In the article from Hear from Entrepreneurs, one respondent called motivation “garbage”? Would you agree or disagree, and why?
  • How is staying motivated as an entrepreneur similar to being motivated to pursue a college degree? Do you think the two are related? How?
  • How would you expect motivation to vary across cultures?[/BOX]

Concept Check

  • Understand the modern approaches to motivation theory.

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  • Book URL: https://openstax.org/books/principles-management/pages/1-introduction
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