motivation research

Motivation Science

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Journal scope statement

Motivation Science is a multi-disciplinary journal that publishes significant contributions to the study of motivation, broadly conceived.

The journal publishes papers on diverse aspects of, and approaches to, the science of motivation, including work carried out in all subfields of psychology, cognitive science, economics, sociology, management science, organizational science, neuroscience and political science.

Primarily, Motivation Science features empirical papers on motivational topics, although theoretical papers and reviews of the literature will also be considered.

Equity, diversity, and inclusion

Motivation Science supports equity, diversity, and inclusion (EDI) in its practices. More information on these initiatives is available under EDI Efforts .

Call for papers

• Call for papers: General

Editor’s Choice

One article from each issue of Motivation Science will be highlighted as an “ Editor’s Choice ” article. Selection is based on the recommendations of the associate editors, the paper’s potential impact to the field, the distinction of expanding the contributors to, or the focus of, the science, or its discussion of an important future direction for science. Editor’s Choice articles are featured alongside articles from other APA published journals in a bi-weekly newsletter and are temporarily made freely available to newsletter subscribers.

Author and editor spotlights

Explore journal highlights : free article summaries, editor interviews and editorials, journal awards, mentorship opportunities, and more.

Prior to submission, please carefully read and follow the submission guidelines detailed below. Manuscripts that do not conform to the submission guidelines may be returned without review.

To submit to the Editorial Office of Guido H. E. Gendolla and Rex A. Wright, please submit manuscripts electronically through the Manuscript Submission Portal in Word Document format (.doc).

Guido H. E. Gendolla University of Geneva

Rex A. Wright University of Texas Dell School of Medicine & University of North Texas, United States

Prepare manuscripts according to the Publication Manual of the American Psychological Association using the 7 th edition. Manuscripts may be copyedited for bias-free language (see Chapter 5 of the Publication Manual ). APA Style and Grammar Guidelines for the 7 th edition are available.

Submit Manuscript

Motivation Science welcomes significant high-quality manuscripts reporting research on diverse aspects of, and approaches to, the science of motivation.

Manuscripts should fall into one of the following categories:

Original Research Articles

These are full-length manuscripts reporting empirical research that advances the comprehension of variables and processes that influence motivation and behavior. Research articles can report more than one empirical study but are not required to do so. Preference will be given to reports that present theory-driven research involving tests of clearly derived hypotheses and findings with straightforward and substantive implications. Replication studies are welcome if they provide conclusive results. There is no space limit for research articles, but manuscripts should typically range between 2,500 and 7,000 words, exclusive of references, figures, and tables.

Original Research Brief Reports

These are abbreviated manuscripts succinctly reporting empirical research that advances the comprehension of variables and processes that influence motivation and behavior. Original research brief reports should report one empirical study or a short series of studies with similar designs and methods. Preference will be given to reports that present theory-driven research involving tests of clearly derived hypotheses and findings with straightforward and substantive implications. Replication studies are welcome if they provide conclusive results. The space limit for brief reports is 2,500 words, exclusive of references, figures, and tables.

Conceptual Articles

These are full-length theoretical papers and literature reviews that can — but are not required to — apply meta-analytic techniques. Preference will be given to analyses and reviews that have straightforward and substantive implications. There is no space limit for conceptual articles, but manuscripts should typically rage between 2,500 and 7,000 words, exclusive of references, figures, and tables.

Conceptual Brief Reports

These are abbreviated theoretical papers and literature reviews that can — but are not required to — apply meta-analytic techniques. Preference will be given to analyses and reviews that have straightforward and substantive implications. Conceptual brief reports also can involve reactive commentary (e.g., to a research or conceptual article). The space limit for conceptual brief reports is 2,500 words, exclusive of references, figures, and tables.

Research Methods in Motivation Science Articles

These manuscripts are intended to draw attention to methodological developments relevant to the scientific study of motivation. They should aim to enhance the use of techniques and insights that advance motivation science and its application. Examples might include articles (1) that describe techniques for validating new research instruments, (2) that introduce new quantitative methods, or (3) that debate important methodological issues. Articles should be accessible to non-expert readers with doctoral level training and avoid use of unnecessary technical content. They also should make clear how their content advances theory and/or practice. Preference will be given to well-constructed and documented reports that have substantive implications. There is no space limit for Methods articles, but manuscripts should typically range between 2,500 and 7,000 words, exclusive of references, figures, and tables.

Out-of-the-Box Articles

These are brief contributions that might attract broad interest but do not fit neatly into preceding submission categories. Submissions will include no more than 2,500 words and can involve a variety of formats. Examples include short thought pieces, humor pieces, pieces concerned with history, and pieces concerned with contemporary issues. Contributions might involve personal profiles (e.g., of important figures), interviews, and even substantive fictional depictions.

Masked Review Policy

Masked review, which means that the identities of both authors and reviewers are masked, is optional for Motivation Science . Authors should note in their cover letters whether they have opted for masked or unmasked review.

Masked Review

Authors who desire masked review should make every effort to see that the manuscript itself contains no clues to their identities:

• Authors should never use first person ( I, my, we, our ) when referring to a study conducted by the author(s) or when doing so reveals the authors' identities (e.g., "in our previous work, Johnson et al. (1998) reported that..."). Instead, references to the authors' work should be in third person (e.g., "Johnson et al. (1998) reported that...").
• The authors' institutional affiliations should also be masked in the manuscript.
• Include the title of the manuscript along with all authors' names and institutional affiliations in the cover letter.
• The first page of the manuscript should omit the authors' names and affiliations but should include the title of the manuscript and the date it is submitted.
• Responsibility for masking the manuscript rests with the authors; manuscripts will be returned to the author if not appropriately masked. If the manuscript is accepted, authors will be asked to make changes in wording so that the paper is no longer masked.
• After masked review, please ensure that the final version for production includes a byline and full author note for typesetting.

Research Transparency and Openness

Motivation Science encourages both methodological and data transparency to ensure the reproducibility of research results. Thus, we ask authors to ensure their manuscripts meet certain standards aligned with APA Style Journal Article Reporting Standards (JARS). These items include:

• Sample Size and Stopping Rules : Authors must describe the sample size, power, and precision, including:

▪ Intended sample size

▪ Achieved sample size, if different from the intended sample size

▪ Determination of sample size, including:

◦ Power analysis, or methods used to determine precision of parameter estimates

◦ Explanation of any interim analyses and stopping rules employed

• Reporting the full methods  for empirical studies, including all manipulations, measures, and eventual data exclusions.
• Reporting in the author note when data stem from related research.  APA Style stipulates that authors must include any disclosures of data stemming from related research in the author note. The original findings, if published, should be referenced in an in-text citation.

▪ If data stem from related research, authors should report:

◦ Whether the full methods are available, either as a citation to a published paper or hosted repository.

• Ethical approval: authors must include IRB or related institution ethical approval for the reported research. For empirical studies, if no ethical approval was sought, authors must explain why.
• Open Data: Authors for Motivation Science are encouraged to make their data and stimulus materials publicly available, if possible, by providing a link in their submission to a relevant data repository.

Making data and materials publicly available can increase the impact of the research, enabling future researchers to incorporate the original authors’ work in model testing, replication projects, and meta-analyses, in addition to increasing the transparency of the research.

Consideration for publication in Motivation Science does not require public posting, so it is at the author(s)’ discretion to decide what is best for their projects in terms of public data, materials, and conditions on their use.

Data Availability Statement :

Regardless of whether or not they choose to make the article data openly available, authors must include a Data Availability Statement in the author note. Authors must indicate whether the data and code reported in the manuscript will be made available or provide a reason for not sharing the data. The link to the permanent repository for the dataset and codebook (or the brief statement explaining why data are not being shared) must be included in the author note.

Please note that the APA Publication Manual (7th ed.) does note that researchers must make their data available to permit other qualified professionals to confirm the analyses and results, upon request. Therefore, making data openly available now may save the authors time later on.

Authors opting for masked review should ensure their datasets and supporting materials are anonymized prior to submission. The Open Science Framework provides instructions for creating anonymized links to data sets, codebooks, and relevant scripts or materials to protect the integrity of the masked review process.

Should the manuscript be accepted, links to the data set, codebook, and supporting materials (now made non-anonymized) should be included in the author note, per the Data Availability Statement requirements described above.

Authors should review the updated JARS for quantitative , qualitative , and mixed methods research before submitting. These standards offer ways to improve transparency in reporting to ensure that readers have the information necessary to evaluate the quality of the research and to facilitate collaboration and replication. For further resources, including flowcharts, see the Journal Article Reporting Standards (JARS) website .

Author contribution statements using CRediT

The APA  Publication Manual  (7th ed.)  stipulates that “authorship encompasses…not only persons who do the writing but also those who have made substantial scientific contributions to a study.” In the spirit of transparency and openness,  Motivation Science  has adopted the  Contributor Roles Taxonomy (CRediT)  to describe each author's individual contributions to the work. CRediT offers authors the opportunity to share an accurate and detailed description of their diverse contributions to a manuscript.

Submitting authors will be asked to identify the contributions of all authors at initial submission according to this taxonomy. If the manuscript is accepted for publication, the CRediT designations will be published as an Author Contributions Statement in the author note of the final article. All authors should have reviewed and agreed to their individual contribution(s) before submission.

CRediT includes 14 contributor roles, as described below:

• Conceptualization:  Ideas; formulation or evolution of overarching research goals and aims.
• Data curation:  Management activities to annotate (produce metadata), scrub data and maintain research data (including software code, where it is necessary for interpreting the data itself) for initial use and later reuse.
• Formal analysis:  Application of statistical, mathematical, computational, or other formal techniques to analyze or synthesize study data.
• Funding acquisition:  Acquisition of the financial support for the project leading to this publication.
• Investigation:  Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection.
• Methodology:  Development or design of methodology; creation of models.
• Project administration:  Management and coordination responsibility for the research activity planning and execution.
• Resources:  Provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing resources, or other analysis tools.
• Software:  Programming, software development; designing computer programs; implementation of the computer code and supporting algorithms; testing of existing code components.
• Supervision:  Oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team.
• Validation:  Verification, whether as a part of the activity or separate, of the overall replication/reproducibility of results/experiments and other research outputs.
• Visualization:  Preparation, creation and/or presentation of the published work, specifically visualization/data presentation.
• Writing—original draft:  Preparation, creation and/or presentation of the published work, specifically writing the initial draft (including substantive translation).
• Writing—review and editing:  Preparation, creation and/or presentation of the published work by those from the original research group, specifically critical review, commentary or revision—including pre- or post-publication stages.

Authors can claim credit for more than one contributor role, and the same role can be attributed to more than one author.

Manuscript Preparation

Review APA's Journal Manuscript Preparation Guidelines before submitting your article.

Double-space all copy. Other formatting instructions, as well as instructions on preparing tables, figures, references, metrics, and abstracts, appear in the Manual . Additional guidance on APA Style is available on the APA Style website .

Below are additional instructions regarding the preparation of display equations, computer code, and tables.

Display Equations

We strongly encourage you to use MathType (third-party software) or Equation Editor 3.0 (built into pre-2007 versions of Word) to construct your equations, rather than the equation support that is built into Word 2007 and Word 2010. Equations composed with the built-in Word 2007/Word 2010 equation support are converted to low-resolution graphics when they enter the production process and must be rekeyed by the typesetter, which may introduce errors.

To construct your equations with MathType or Equation Editor 3.0:

• Go to the Text section of the Insert tab and select Object.
• Select MathType or Equation Editor 3.0 in the drop-down menu.

If you have an equation that has already been produced using Microsoft Word 2007 or 2010 and you have access to the full version of MathType 6.5 or later, you can convert this equation to MathType by clicking on MathType Insert Equation. Copy the equation from Microsoft Word and paste it into the MathType box. Verify that your equation is correct, click File, and then click Update. Your equation has now been inserted into your Word file as a MathType Equation.

Use Equation Editor 3.0 or MathType only for equations or for formulas that cannot be produced as Word text using the Times or Symbol font.

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Because altering computer code in any way (e.g., indents, line spacing, line breaks, page breaks) during the typesetting process could alter its meaning, we treat computer code differently from the rest of your article in our production process. To that end, we request separate files for computer code.

In Online Supplemental Material

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Abstract and Keywords

All manuscripts must include an abstract containing a maximum of 250 words typed on a separate page. After the abstract, please supply up to five keywords or brief phrases.

List references in alphabetical order. Each listed reference should be cited in text, and each text citation should be listed in the References section.

Examples of basic reference formats:

Journal Article

McCauley, S. M., & Christiansen, M. H. (2019). Language learning as language use: A cross-linguistic model of child language development. Psychological Review , 126 (1), 1–51. https://doi.org/10.1037/rev0000126

Authored Book

Brown, L. S. (2018). Feminist therapy (2nd ed.). American Psychological Association. https://doi.org/10.1037/0000092-000

Chapter in an Edited Book

Balsam, K. F., Martell, C. R., Jones. K. P., & Safren, S. A. (2019). Affirmative cognitive behavior therapy with sexual and gender minority people. In G. Y. Iwamasa & P. A. Hays (Eds.), Culturally responsive cognitive behavior therapy: Practice and supervision (2nd ed., pp. 287–314). American Psychological Association. https://doi.org/10.1037/0000119-012

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Guido H. E. Gendolla, PhD University of Geneva, Switzerland

Rex A. Wright, PhD University of Texas Dell School of Medicine & University of North Texas, United States

Associate editors

Marylène Gagné, PhD Curtin University, Australia

Catalina Kopetz, PhD Wayne State University, United States

Joyce S. Pang, PhD Nanyang Technological University, Singapore

Consulting editors

Nicola Baumann, PhD University of Trier, Germany

Roy F. Baumeister, PhD University of Queensland, Australia, & Florida State University, United States

Erik Bijleveld, PhD Radboud University, the Netherlands

Veronika Brandstätter, PhD University of Zurich, Switzerland

C. Miguel Brendl, PhD University of Basel, Switzerland

Marcus Crede, PhD Iowa State University, United States

Ruud Custers, PhD Utrecht University, the Netherlands

David Dunning, PhD University of Michigan, United States

Jacquelynne Sue Eccles, PhD University of California, Irvine, United States

Andrew J. Elliot, PhD University of Rochester, United States

Ayelet Fishbach, PhD University of Chicago, United States

Alexandra M. Freund, PhD University of Zurich, Switzerland

Malte Friese, PhD Saarland University, Germany

James W. Fryer, PhD State University of New York at Potsdam, United States

Philip A. Gable, PhD University of Delaware, United States

Peter M. Gollwitzer, PhD New York University, United States

Ana Guinote, PhD University College London, United Kingdom

Martin S. Hagger, PhD University of California, Merced, United States, & University of Jyväskylä, Finland

Judith M. Harackiewicz, PhD University of Wisconsin – Madison, United States

Eddie Harmon-Jones, PhD University of New South Wales, Australia

Jutta Heckhausen, PhD University of California at Irvine, United States

Marlone Henderson, PhD University of Texas at Austin, United States

Marie Hennecke, PhD Ruhr-University Bochum, Germany

Thomas M. Hess, PhD North Carolina State University, United States

Suzanne E. Hidi, PhD University of Toronto, Canada

E. Tory Higgins, PhD Columbia University, United States

Wilhelm Hofmann, PhD University of Cologne, Germany

Bernhard Hommel, PhD Leiden University, the Netherlands

Julie Y. Huang, PhD Stony Brook University, United States

Veronika Job, PhD University of Zurich, Switzerland

John T. Jost, PhD New York University, United States

Miguel Kazén, PhD University of Osnabrück, Germany

Hugo Martin Kehr, PhD Technical University Munich School of Management, Germany

Daria Knoch, PhD University of Bern, Switzerland

Richard Koestner, PhD McGill University

Sander L. Koole, PhD Vrije Universiteit Amsterdam, the Netherlands

Małgorzata Kossowska, Prof Jagiellonian University, Poland

Arie Wladimir Kruglanski, PhD University of Maryland, United States

Gary Latham, PhD University of Toronto, Canada

Edwin A. Locke, PhD University of Maryland, United States

Samuele Marcora, PhD University of Kent, United Kingdom

Marina Milyavskaya, PhD Carleton University, Canada

Gordon Moskowitz, PhD Lehigh University, United States

Nikos Ntoumanis, PhD Curtin University, Australia

Gabriele Oettingen, PhD New York University, United States

Giuseppe Pantaleo, PhD San Raffaele University of Milan, Italy

Erika A. Patall, PhD USC Rossier School of Education, United States

Mathias Pessiglione, PhD Institut du Cerveau et de la Moelle Epinière, France

Luiz Pessoa, PhD University of Maryland at College Park, United States

Deborah A. Prentice, PhD Princeton University, United States

Johnmarshall Reeve, PhD Australian Catholic University, Australia

K. Ann Renninger, PhD Swarthmore College, United States

Michael Richter, PhD Liverpool John Moores University, United Kingdom

Richard M. Ryan, PhD Institute for Positive Psychology and Education at Australian Catholic University, Australia

Kaspar Schattke, DPhil Université du Québec à Montréal, Canada

Brandon J. Schmeichel, PhD Texas A&M University, United States

Abigail A. Scholer, PhD University of Waterloo, Canada

Oliver C. Schultheiss, DPhil Friedrich-Alexander University, Germany

Paschal Sheeran, PhD University of North Carolina at Chapel Hill, United States

Kennon M. Sheldon, PhD University of Missouri, United States

Paul J. Silvia, PhD University of North Carolina at Greensboro, United States

Robert J. Vallerand, PhD Université du Québec à Montréal, Canada

Lotte Frederike van Dillen, PhD Leiden University, the Netherlands

Jack van Honk, PhD Utrecht University, the Netherlands

Nico W. Van Yperen, PhD University of Groningen, the Netherlands

Thomas L. Webb, PhD The University of Sheffield, United Kingdom

Bernard Weiner, PhD University of California at Los Angeles, United States

Abstracting and indexing services providing coverage of Motivation Science

• APA PsycInfo

Special issue of the APA journal Motivation Science, Vol. 8, No. 2, June 2022. This special issue collection represents an effort to further expand our knowledge and understanding of aggression from a multidisciplinary perspective.

Special issue of the APA journal Motivation Science, Vol. 3, No. 3, September 2017. The issue presents a set of diverse papers that examine the topic of motivation from multiple points of view.

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This journal offers masked peer review (where both the authors’ and reviewers’ identities are not known to the other). Research has shown that masked peer review can help reduce implicit bias against traditionally female names or early-career scientists with smaller publication records (Budden et al., 2008; Darling, 2015).

• September 2022: A Fresh Term of Service at Motivation Science (PDF, 130KB)
• March 2020: Updates and News From Motivation Science
• March 2018: Updates on the Development of Motivation Science
• September 2016: Gathering the Diaspora: Aims and Visions for Motivation Science (PDF, 17KB)

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On what motivates us: a detailed review of intrinsic v. extrinsic motivation

Laurel s. morris.

1 Department of Psychiatry, Depression and Anxiety Center for Discovery and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA

Mora M. Grehl

2 Department of Psychology, Temple University, Philadelphia, PA 19122 USA

Sarah B. Rutter

Marishka mehta, margaret l. westwater.

3 Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510 USA

Motivational processes underlie behaviors that enrich the human experience, and impairments in motivation are commonly observed in psychiatric illness. While motivated behavior is often examined with respect to extrinsic reinforcers, not all actions are driven by reactions to external stimuli; some are driven by ‘intrinsic’ motivation. Intrinsically motivated behaviors are computationally similar to extrinsically motivated behaviors, in that they strive to maximize reward value and minimize punishment. However, our understanding of the neurocognitive mechanisms that underlie intrinsically motivated behavior remains limited. Dysfunction in intrinsic motivation represents an important trans-diagnostic facet of psychiatric symptomology, but due to a lack of clear consensus, the contribution of intrinsic motivation to psychopathology remains poorly understood. This review aims to provide an overview of the conceptualization, measurement, and neurobiology of intrinsic motivation, providing a framework for understanding its potential contributions to psychopathology and its treatment. Distinctions between intrinsic and extrinsic motivation are discussed, including divergence in the types of associated rewards or outcomes that drive behavioral action and choice. A useful framework for understanding intrinsic motivation, and thus separating it from extrinsic motivation, is developed and suggestions for optimization of paradigms to measure intrinsic motivation are proposed.

Introduction

Motivation is an integral component of human experience. Children spontaneously explore novel items, and adults autonomously engage in new hobbies, even in the absence of clear extrinsic reinforcers. Thus, not all actions are driven by tangible external stimuli or outcomes, known as ‘extrinsic’ motivation, but are driven by more internal drivers, known as ‘intrinsic’ motivation, where the activity is perceived as its own outcome.

Intrinsically motivated behaviors are computationally similar to extrinsically motivated behaviors, in that they strive to maximize goal attainment and minimize punishment, represented mathematically as value and effort cost functions, respectively (Gottlieb, Lopes, & Oudeyer, 2016 ). However, subjective internal value functions are difficult to characterize, and our understanding of how they are computed and integrated is limited (Gottlieb et al., 2016 ).

Dysfunction in intrinsic motivation represents an important transdiagnostic facet of psychiatric symptomology, which is often classified as distinct psychological constructs, such as apathy in neurological disorders, anhedonia in depression, and negative symptoms in schizophrenia. Each of these symptom domains may be underpinned by a shared dysfunction of intrinsic motivation, and interventions targeting intrinsic motivation have the potential to improve treatment outcomes for affected individuals.

However, due to a lack of clear consensus, the contribution of intrinsic motivation to psychiatric disorders remains poorly understood. This review aims to provide an overview of the conceptualization, measurement, and neurobiology of intrinsic motivation, providing a framework for understanding the potential contributions to psychopathology and its treatment.

Historical conceptualizations of intrinsic motivation

During the early 20th century, prominent descriptions of motivation were at odds with each other. Woodworth ( 1918 ) suggested that intrinsic motivation governed activities perpetuated by their own ‘native drive’, whereas Thorndike ( 1911 ) and Watson ( 1913 ) argued that external stimuli governed behavior. Also centered on internal drives, Hull's ( 1943 ) ‘drive theory’ posited that all behaviors were performed to seek or avoid primary biological states, including hunger or pain. However, the drive theory could not explain many behavioral anomalies, such as hungry rats withstanding painful electric shocks to explore a novel environment (Nissen, 1930 ), or rhesus monkeys performing a puzzle task for no biological reason or external reinforcer (Harlow, 1950 ). By narrowly presuming that biological states drive all behavior, drive theory failed to account for instances in which an organism prioritizes higher-order cognitive drives over physiological ones.

The shortcomings of drive theory led to the emergence of alternate theories of intrinsic motivation. Some argued that homeostatic maintenance of optimal biological or cognitive states (Hebb, 1955 ; McClelland & Clark, 1953 ; McClelland, Atkinson, Clark, & Lowell, 1967 ), or mitigation of incongruency or uncertainty (Festinger, 1957 ; Kagan, 1972 ), drove behavior. However, these theories emphasized external stimuli or cognitive representations of external goal states as key drivers of behavior. In the mid-to-late 20th century, several models underscored the importance of novelty-seeking, interest, and autonomy in driving intrinsic motivation. Novelty-seeking was suggested to energize approach behavior via curiosity and exploration that leads to skill mastery, information attainment, or learning (Kaplan & Oudeyer, 2007 ). Interest and enjoyment in an activity might boost intrinsic motivation by engendering ‘flow’, a prolonged state of focus and enjoyment during task engagement that stretches one's skillset (Csikszentmihalyi, 1975 ; Nakamura & Csikszentmihalyi, 2009 ). Finally, self-determination theory (Deci & Ryan, 1980 ) proposed that human needs for competence, achievement, and autonomy drive intrinsic motivation, aligning with observations that intrinsic motivation stems from an internal perceived autonomy during task engagement (DeCharms, 1968 ; Lamal, 2003 ). These models highlight the role of achievement and perceived autonomy (DeCharms, 1968 ) in driving intrinsic motivation, coinciding with current computational frameworks of intrinsic reward (Chew, Blain, Dolan, & Rutledge, 2021 ; Murayama, Matsumoto, Izuma, & Matsumoto, 2010 ).

The introduction of external goals: a shift to extrinsic motivation

While intrinsic motivation has been proposed to be divorced from external reinforcers, our understanding of motivation has been led largely by using external reinforcers as conceptual and experimental tools. Here, we briefly review historical perspectives on external drivers of motivated behavior, outlining prominent goal- and action-focused models of extrinsic motivation.

Early psychological models of extrinsic motivation suggested that ‘will’ and ‘intention’ fostered goal achievement, emphasizing the influence of goal expectation on action and control (Lewin, 1951 ; Tolman, 1932 ). Within this framework, environmental features, as well as an individual's internal state or memory, determine their actions when pursuing a goal, or, more specifically, the cognitive representation of a goal (Kagan, 1972 ). This requires multiple cognitive representations to be developed, maintained, and updated, with a particular reliance on external stimuli and learning (Deci, Koestner, & Ryan, 1999 ; Kagan, 1972 ; Kagan & Moss, 1983 ).

Alongside psychological model development, economic models of motivation emerged. These models propose that extrinsic goals, or incentives, elicit motivated behavior via a cost-benefit analysis, where motivated choice occurs when benefits outweigh costs. More recently, behavioral economics has considered how individual personality traits, biases, and irrationalities influence motivated behavior (Strombach, Strang, Park, & Kenning, 2016 ). A recent model (Strombach et al., 2016 ) incorporates various factors into the classical cost-benefit analysis, including traditional intrinsic (e.g. satisfaction) and extrinsic drivers (e.g. money), with negative influences from costs (e.g. effort, pain), which are merged into a single dynamic, subjective and state-dependent factor that drives motivated behavior. Though this approach is powerful, the explicit focus on incentives provides limited explanatory power for various paradoxical behaviors, including rodents overcoming the high cost to self-stimulate certain brain regions (e.g. nucleus accumbens; Nac) or extrinsic reinforcers' dampening effect on intrinsic motivation.

In reinforcement learning models of decision-making, an organism, or agent, learns which actions maximize total reward. This process has been formalized within computational sciences and modern artificial intelligence systems (Sutton & Barto, 1981 ; Witten, 1977 ), where learning and decision-making depend on an extrinsic outcome. One theory suggests that motivated action is driven solely by a need to reduce reward prediction errors (RPEs; Kaplan and Oudeyer, 2007 ), or the mismatch between expectation and outcome (Montague, Dayan, & Sejnowski, 1996 ; Schultz et al., 1997 ). RPEs can also be conceptualized as valuation signals for novel outcomes or unexpected stimuli. RPE-based learning then drives motivated behavior, or action choice, but even if the agent displays intact encoding of action or outcome value, motivated behavior can be dampened by reduced novelty. This highlights the role of novelty, expectation and prediction in learning per se , rather than choice valuation.

In action-focused models of motivation, incentives can trigger approach or avoidance behavior by signaling a potential goal state (Berridge, Robinson, & Aldridge, 2009 ). Incentive motivation thus relies on expectancy, probability, and value of outcomes, which are thought to dictate behavioral choice and decision-making. While greater reliance on stimulus-outcome rather than stimulus-response contingencies has led some to describe incentive motivation as proactive (Beckmann & Heckhausen, 2018 ), others have characterized it as reactive due to the central role of learning from past experience (Bolles, 1972 ). Reliance on an expected outcome was central to behaviorism (Watson, 1913 , 1930 ) and operant conditioning (Skinner, 1938 ), which assume that actions are driven by a reinforcer, and instrumental value is assigned to the behavior itself. Stimulus-response pairs dominate behaviorism and modern theories of habitual behavior (Gläscher, Daw, Dayan, & O'Doherty, 2010 ), where the dependency on previously reinforced actions ultimately governs motivated choice (de Wit et al., 2011 ; Gillan, Robbins, Sahakian, van den Heuvel, & van Wingen, 2016 ; Voon et al., 2014 ). However, this renders behaviors as repetitive, insensitive to punishment and divorced from goals (Robbins, Gillan, Smith, de Wit, & Ersche, 2012 ). Therefore, these action-focused models of motivated behavior almost entirely discount intrinsic motivation since extrinsic motivators usurp control of behavior.

Several limitations of extrinsic motivation models must be considered when attempting to characterize intrinsic motivation. First, for cost-benefit analysis and reinforcement learning, an internal representation of the outcome must first be learned, which requires previous experience of the goal. However, intrinsic motivation can occur for novel outcomes, or behaviors that are uncertain or ambiguous. Second, motivation can occur for activities that may already be fully predictable, marking a significant limitation for reinforcement-learning models of motivation, which assume that reward prediction errors drive learning for motivated action. Third, these frameworks cannot fully explain spontaneous novelty seeking or exploratory behavior, in which no external reward is expected and no cost is overcome (Deci et al., 1999 ; Marsden, Ma, Deci, Ryan, & Chiu, 2014 ).

Separating and integrating intrinsic and extrinsic motivation

A key question is whether intrinsic and extrinsic motivation can, or should, be experimentally or theoretically separated. There is some evidence that they are dissociable constructs at the neural level. The most compelling support comes from case reports of patients with basal ganglia lesions who developed ‘psychic akinesia’, a syndrome characterized by difficulty with self-generated action initiation but no difficulty in performing complex cognitive or motor tasks when prompted (Laplane, Baulac, Widlocher, & Dubois, 1984 ; Lugaresi, Montagna, Morreale, & Gallassi, 1990 ). In patients with alien hand syndrome, medial prefrontal and anterior cingulate cortex (ACC) lesions lead to a loss of intentional motor control, whereas (pre)-supplementary motor area lesions lead to impairments in implementing motor intentions (Brugger, Galovic, Weder, & Kägi, 2015 ; Nachev, Kennard, & Husain, 2008 ). Preclinical findings further show that photostimulation of GABAergic amygdala projections modulates extrinsic motivation without affecting intrinsically motivated behavior (Seo et al., 2016 ). Together, these findings suggest that intrinsic and extrinsic motivation reflect different cortico-striatal-limbic circuits.

Behavioral research primarily supports the view that intrinsic and extrinsic motivation are partially distinct, interacting processes. For example, if the motivation for intrinsic and extrinsic goals were independent constructs, they might demonstrate an additive or subtractive effect on each other (Woodworth, 1921 ). Indeed, the expectation (Liu & Hou, 2017 ) and experience (Badami, VaezMousavi, Wulf, & Namazizadeh, 2011 ) of an extrinsic reinforcer can increase intrinsic motivation. However, reports of the ‘undermining effect’, in which an external reinforcer reduces intrinsic motivation (Cerasoli, Nicklin, & Ford, 2014 ; Deci, 1971 ; Deci, Benware, & Landy, 1974 ; Lepper & Greene, 1978 ; Lepper, Greene, & Nisbett, 1973 ) have sparked debate over how extrinsic reinforcers affect internally-motived behaviors (Cameron & Pierce, 2002 ; Lamal, 2003 ; Lepper, Keavney, & Drake, 1996 ). One explanation for the undermining effect suggests that the presence of an external reinforcer shifts one's perception of the locus of control over the behavior from internal to external (Deci & Ryan, 1980 ). This implicates a key role of agency, or the belief of action ownership, in intrinsic motivation. While controversial, mounting evidence supports this account of the undermining effect, where various extrinsic motivators (e.g. food, social observation; Ryan, 1982 ) decrease intrinsic motivation when their delivery is contingent on task-performance.

A useful framework for parsing motivated action into intrinsic and extrinsic is the Rubicon model of action phases (Heckhausen & Heckhausen, 2018 ; Heckhausen, 1989 ). Within this framework, pre-decisional option deliberation occurs, which is followed by choice intention formation and planning, volitional action, outcome achievement, and evaluation ( Fig. 1 ). Husain and Roiser ( 2018 ) recently proposed a complementary model to deconstruct apathy and anhedonia into underlying cognitive processes, including option generation, anticipation, action initiation, prediction, consumption and learning. This parcellation broadly reflects the five main stages of the Rubicon model: (1) pre-decisional deliberation ( option generation ); (2) intention formation, planning, initiation ( anticipation ); (3) volitional action ( action initiation, prediction ), (4) outcome achievement ( consumption ); and (5) evaluation ( learning ; Figure 1 ). Within these overlapping frameworks, the initial pre-decisional deliberation/option generation phase represents the point at which intrinsic and extrinsic facets of motivation diverge, as early drivers of behavior can be intrinsic (e.g. enjoyment, interest, exploration) or extrinsic (e.g. social reward). The differences between these early drivers highlight a key distinction between intrinsic and extrinsic motivation, in which the former is a fundamentally proactive process and the latter reactive.

Schematic framework for parsing motivated action. Motivated decision-making and action is parsed into separate phases of choice, action and outcome valuation, combining and building upon separate frameworks including the Rubicon model of action phases, well-established computational mechanisms and a recent cognitive framework describing anhedonia and apathy. During choice valuation, pre-decisional deliberation includes option generation, a cost-benefit analysis and option selection. Intrinsic and extrinsic motivation diverges during this early choice valuation phase. Once choice valuation has been computed and an option selected, planning and anticipation occurs. During action valuation, volitional action is initiated and action sustainment or acceleration is maintained. During outcome valuation, outcome achievement and consumption ensue, followed by evaluation based on learning via prediction error (PE) updating. Created with BioRender.com .

If a behavior were intrinsically motivated, the pre-decisional deliberation phase might be determined by biological drives, the need to restore homeostasis (Hebb, 1949 ; Hull, 1943 ), or a state of incongruency resolution (Festinger, 1957 ; Kagan, 1972 ) as described by early theories of intrinsic motivation. In contemporary frameworks, novelty-seeking, exploration, or interest in learning or achievement would render subsequent actions as intrinsically motivated. If a behavior were extrinsically motivated, this pre-decisional deliberation phase would represent the cost-benefit analysis in economic models, prediction-error minimization in reinforcement learning, or effort-reward trade-off computation. Under incentive motivation and behaviorist theories, the pre-decisional deliberation phase would be triggered by conditioned stimuli making conscious deliberation unnecessary and inefficient.

A combination of intrinsic and extrinsic factors likely enters into the pre-decisional deliberation phase to guide motivated behavior ( Fig. 1 ). Although intrinsic and extrinsic motivation are conceptually distinct processes, attempts to formally define them have identified several mechanisms by which they interact, leading to questions about their dissociability. Since they can interact in an additive or subtractive fashion, they may indeed be separate, independent drivers of behavior that are amalgamated during a pre-decisional deliberation phase of behavioral choice.

Measuring intrinsic motivation

Human behavior.

Early attempts to quantify intrinsic motivation were largely based on behavioral observation, wherein intrinsic motivation was measured as free choice of an activity in the absence of an external stimulus or performance rating (Butler & Nisan, 1986 ; Daniel & Esser, 1980 ; Liu & Hou, 2017 ). These studies also implemented self-report measures of participants' interest or enjoyment in an activity. While such measures do capture intrinsic motivation as inherent task enjoyment, they are limited by their qualitative and indirect nature, as well as by variability in participant insight. However, more objective measures are difficult to develop due to the inherently unobservable nature of intrinsic motivation.

Since spontaneous novelty-seeking and exploratory behavior reflect intrinsic motivation, one candidate objective measure may be the explore-exploit paradigm (Gittins & Jones, 1979 ; Robbins, 1952 ). In explore-exploit foraging tasks, participants must choose among various options and either exploit a previously reinforced choice or explore a novel alternative option. An individual's tendency to either explore an environment or exploit their pre-existing knowledge is influenced by perseverance (Von Culin et al., 2014 ), which acts as an indicator of confidence in the absence of immediate reward. Healthy adults flexibly employ a mix of exploitative and exploratory choices, where striatal and prefrontal dopamine signaling is proposed to drive exploration and exploitation, respectively (Badre, Doll, Long,, & Frank,, 2012 ; Daw, O'Doherty, Dayan, Seymour, & Dolan, 2006 ; Mansouri, Koechlin, Rosa, & Buckley, 2017 ). While these tasks capture one's willingness to trade-off exploratory v. exploitative behaviors, they do not measure free-choice exploratory behavior in the absence of explicit reinforcers, which would be most consistent with intrinsic motivation.

Paradigms that allow an individual to choose to explore an environment without extrinsic reinforcers, or to engage in a previously enjoyable or interesting activity, would more closely index intrinsic motivation. Additionally, outcomes that relate to achievement or autonomy, without socially rewarding feedback or monetary outcomes, would also putatively engage intrinsic motivation. Task parameters related to exploration, enjoyment, achievement, and autonomy can each be modulated and computationally modeled to determine their effects on free choice or behavioral activation vigor.

Current computational approaches depend on modeling decision-making, outcome learning, or action-outcome associations to drive our understanding of motivation. Traditional decision-making models often rely on softmax functions to compute values of available actions (Wilson & Collins, 2019 ), where action selection is based on the ‘policy’ of the best outcome. Computationally, an action selection process computes the probability of an action occurring in any state and the expected reward. A policy is developed based on the assumption that motivated actions are performed to increase the probability of rewards and decrease the probability of punishment. Yet, in everyday life, our actions can be motivated by an arbitrary cue that may signal an internal rewarding state. For example, a standard algorithm solving for motivated action assumes that all actions have equal probability, yet this discounts the unknown drivers and evaluators of internal rewards. Hence, they act as limiting factors to the applicability of decision-making models in studies of intrinsic motivation.

Neuroimaging

Functional neuroimaging [e.g., functional magnetic resonance imaging (fMRI), electroencephalography (EEG)] offers a measurement modality that may be particularly apt for the study of internally driven processes like intrinsic motivation. Research using fMRI has characterized the neural correlates of various internal processes that lack clear behavioral indicators (e.g. rumination, emotion regulation, pain perception; Zhou et al., 2020 ; Wagner, N'Diaye, Ethofer, and Vuilleumier, 2011 ), yet few studies have assessed the neural correlates of intrinsic motivation in humans, which likely reflects the limitations in its behavioral measurement. Studies have largely assessed intrinsic motivation via comparisons with neural responses to extrinsic reinforcers during fMRI, which can be correlated with self-reported intrinsic motivation (Bengtsson, Lau, & Passingham, 2009 ; Chew et al., 2021 ; Linke et al., 2010 ). Despite the relative paucity of neuroimaging studies that clearly separate intrinsic v. extrinsic motivation, existing work provides preliminary insight into the neural circuitry of intrinsic motivation.

First, extrinsic reinforcers have elicited amygdala, ACC, ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), and ventral striatal (VS) or Nac activity in healthy subjects that was associated with higher self-reported extrinsic motivation but lower self-reported intrinsic motivation (Linke et al., 2010 ). This could suggest that intrinsic motivation relates to a lower sensitivity of these regions to extrinsic reinforcers, general deactivation of these regions, or that the dampening impact of extrinsic reinforcers on intrinsic motivation is subserved by these regions. Others report that intrinsic motivation (operationalized as the amount of free-time spent on a puzzle-task, which did not relate to task enjoyment, interest, or accuracy), was associated with deactivation in the amygdala, dorsal ACC, dorsomedial striatum, and insula during puzzle-task onset (Marsden et al., 2014 ). This is another piece of evidence linking neural deactivation to intrinsic motivation; however, since these tasks were not related to traditional ‘intrinsic motivators’ like task enjoyment, findings may relate to boredom-reduction behavior that might be more related to punishment avoidance rather than intrinsic motivation per se .

Bengtsson et al. ( 2009 ) operationalized intrinsic motivation as task-performance with and without explicit experimental observation during fMRI scanning, which boosted self-reported intrinsic motivation. The authors found greater neural activation of ACC, OFC, and lateral prefrontal cortex during task-performance errors when participants were observed (Bengtsson et al., 2009 ). While implicating a similar network of brain regions as prior studies, these findings cannot be divorced from error-related neural activation modulated by task salience (e.g. observed v. not).

In contrast, Murayama et al . ( 2010 ) provide a more optimal operationalization of intrinsic motivation, in which participants performed a task that was previously rated as inherently interesting, and successful task performance served as the intrinsic reward. During fMRI scanning, feedback for both extrinsic (monetary feedback) and intrinsic (accuracy feedback) rewards elicited VS activation. Participants then had the option to perform the same task without feedback, and intrinsic motivation was operationalized as time spent on the second version of the task. During the second session, VS activation was only diminished for extrinsic rewards, which could reflect reduced VS habituation to intrinsic rewards (Murayama et al., 2010 , 2015 ). Additionally, greater reductions in neural responses to extrinsic reinforcers were related to lower intrinsic motivation (i.e. task engagement time outside of the scanner), suggesting that neural habituation to extrinsic reinforcers may relate to lower intrinsic motivation. A recent computational neuroimaging study modeled intrinsic rewards as successful spatial-motor task performance without experienced errors, which was divorced from learning (Chew et al., 2021 ). This modeling of intrinsic rewards was akin to the accuracy feedback operationalization of Murayama et al . ( 2010 ). Both extrinsic (monetary) reward and intrinsic performance-based rewards (successful task completion) recruited vmPFC activation, which related to subjective happiness (Chew et al., 2021 ). Although limited in their ability to dissociate activation from task performance per se and explicit feedback related to achievement, these studies are the closest examples of objective measures of intrinsic motivation, and they suggest that putative reward-processing regions (VS, vmPFC) encode intrinsic rewards.

Complementary studies have examined how curiosity, or the intrinsic motivation to learn, modulates neural responses and influences memory recall (Gruber, Gelman, & Ranganath, 2014 ; Kang et al., 2009 ). High-curiosity states augment midbrain and v. activity (Gruber et al., 2014 ), as well as bilateral caudate (Kang et al., 2009 ) and anterior insula (Lee & Reeve, 2017 ) responses, which may improve learning and memory. As these paradigms index intrinsic motivation independently from a rewarding outcome, they perhaps provide the strongest support for partially overlapping circuits of extrinsic and intrinsic motivation.

The brain's dopamine system supports a range of appetitive and aversive motivational processes, including behavioral activation, exertion of effort, and sustained task engagement (Diederen & Fletcher, 2020 ; Salamone, Yohn, López-Cruz, San Miguel, & Correa, 2016 ). The mesolimbic pathway, projecting from the ventral tegmental area (VTA) to limbic regions, including the Nac, amygdala, and hippocampus, facilitates reinforcement and associative learning by acting as a ‘Go’ signal for foraging or exploration (Huang, Lv, & Wu, 2016 ). Although it has long been known that dopamine transmission subserves motivational processes, some evidence suggests that it is particularly important for intrinsic motivation. For example, mesolimbic dopamine contributes to exploration for the sake of interest (DeYoung, 2013 ; Panksepp & Moskal, 2008 ), and novel and unexpected stimuli elicit phasic dopamine spikes in rodents (Fiorillo, 2003 ; Hooks & Kalivas, 1994 ; Schultz, 1998 ). In patients with depression, deep-brain stimulation of dopaminergic brain regions including the Nac (Schlaepfer et al., 2007 ) and the mesolimbic dopamine projections from the VTA (Fenoy et al., 2018 ) increased subjective interest in, and motivational energy for, previously enjoyable activities (Schlaepfer et al., 2007 ). Dopamine has also been associated with intrinsically motivated flow states (de Manzano et al., 2013 ; Gyurkovics et al., 2016 ).

However, since VTA dopamine spiking is reduced for expected events (Schultz, 1998 ), it may not be a strong candidate neural mechanism for intrinsic motivation, which can occur for predictable activities. Efforts to reconcile the role of dopamine in learning and motivation suggest that while phasic cell firing signals RPEs (Kim et al., 2020 ), phasic dopamine release and local modulation in key regions, such as the VS/NAc, relates to approach motivation (Berke, 2018 ; Mohebi et al., 2019 ). Indeed, while VTA dopamine cell firing occurs during reward prediction, only NAc dopamine release covaries with reward availability and ramps up during approach and consumption of reward (Mohebi et al., 2019 ). Moreover, increasing dopamine in rodents increases their willingness to exert effort, and this has since been replicated across species, including via pharmacological manipulation in humans (Salamone, Correa, Farrar, & Mingote, 2007 ; Treadway & Zald, 2011 ). This suggests that, while VTA dopamine spiking underpins reward prediction and learning, it is local NAc dopamine release that encodes motivational drive.

Opioids, norepinephrine, and related neurotransmitter systems

Though a comprehensive account of the neurotransmitter systems subserving motivated behavior is beyond the scope of this review, we note that endogenous opioid and cannabinoid systems may uniquely modulate intrinsically motivated behavior. For example, mu- and delta-opioid receptor activation underlies the pleasurable effects of opioid and non-opioid drugs of abuse (Berrendero, Robledo, Trigo, Martín-García, & Maldonado, 2010 ; Trigo, Martin-García, Berrendero, Robledo, & Maldonado, 2010 ), as well as primary reinforcers (Hsu et al., 2013 ; Kelley & Berridge, 2002 ). Activation of mu-opioid receptors has also been shown to mediate motivational states following delta-9-tetrahydrocannabinol (THC) administration in rodents (Ghozland et al., 2002 ), likely via interactions with the mesolimbic dopamine system. Further evidence implicates antidepressant effects of endogenous opioids in both animals and humans (Peciña et al., 2018 ), which many partly reflect improved intrinsic motivation (e.g. time mice spent swimming during the forced swim test; Kastin, Scollan, Ehrensing, Schally, and Coy, 1978 ). Additionally, the endocannabinoid system interacts with both endogenous opioid and dopaminergic systems to influence intrinsic motivation, such as social play (Trezza et al., 2012 ; Trezza & Vanderschuren, 2008 ), and voluntary exercise, in rodents (Dubreucq, Koehl, Abrous, Marsicano, & Chaouloff, 2010 ). Since these systems have been primarily examined in animal models, pharmacological manipulation in humans would be an important next step in delineating the contribution of opioid and endocannabinoid systems to intrinsic v. extrinsic motivation.

Intrinsic motivation and psychiatry: focus on anhedonia

Problems with motivation are observed across many neuropsychiatric disorders, and these often correspond to distinct symptoms ( Table 1 ). This section focuses on anhedonia, a reduced ability to experience pleasure (Ribot, 1986 ), as a prevalent clinical manifestation of deficient intrinsic and extrinsic motivation.

Explicit studies of ‘intrinsic motivation’ in neuropsychiatric disorders

Note: Cohort abbreviations: AUD, alcohol use disorder; FEP, first-episode psychosis; MDD, major depressive disorder; PD, Parkinson's disease; SCZ, schizophrenia; SUDs, substance use disorders; SZA, schizoaffective disorder. Evidence abbreviations: EM, extrinsic motivation; IM, intrinsic motivation.

In the Diagnostic and Statistical Model of Mental Disorders, 5th edition (DSM- 5 ), anhedonia serves as one of two cardinal symptoms of depressive disorders, where it is defined as the ‘loss of interest or pleasure in all, or almost all, activities’, (American Psychiatric Association, 2013 ). The second cardinal symptom relates to persistent depressed mood. Approximately one-third of individuals with depression report clinically significant anhedonia (Pelizza & Ferrari, 2009 ), and these individuals are at-risk for poorer treatment outcomes, including nonresponse, relapse, and increased suicidality, relative to their non-anhedonic peers (Morris, Bylsma, & Rottenberg, 2009 ; Nierenberg et al., 1999 ).

Anhedonia remains an important clinical target that, by definition, implicates perturbations in intrinsically-motivated behavior, yet most empirical studies of anhedonia and motivation have investigated their relationship using extrinsic reinforcers. Findings broadly support theories of reward dysfunction in depression (reviewed by Sescousse, Caldú, Segura, and Dreher, 2013 ; Roiser & Husain, 2018; Borsini, Wallis, Zunszain, Pariante, and Kempton, 2020 ), where anhedonia has been associated with a reduced bias toward a monetary reward in individuals with depression (Liu et al., 2011 ) and their first-degree relatives (Liu et al., 2016 ). Children who are at-risk for depression show reduced VS and anterior insula responses to monetary gains, implicating blunted reward sensitivity as an antecedent to anhedonia (Luking, Pagliaccio, Luby, & Barch, 2016 ). Moreover, vmPFC responses during unexpected reward receipt may indirectly relate to anhedonia in depressed patients by modulating task motivation (Segarra et al., 2016 ). Interestingly, reward sensitivity disturbances in depression might not extend to aberrant reward learning (Huys, Pizzagalli, Bogdan, & Dayan, 2013 ) where adults with moderate depression show intact VS RPE-signaling during probabilistic learning (Rutledge et al., 2017 ). Nevertheless, there have been suggestions that perturbations in domains more related to intrinsic motivation, such as model-based future planning or effort initiation and invigoration, may be key in underlying anhedonia (Berwian et al., 2020 ; Cooper, Arulpragasam, & Treadway, 2018 ; Rutledge et al., 2017 ). Finally, affect can also alter both the valence and evaluation of an activity, which can, in turn, modulate the likelihood of selecting a more inherently interesting task (Isen & Reeve, 2006 ). Anhedonic individuals have more pessimistic likelihood estimates and reduced positive affective forecasts relative to controls while also demonstrating greater reliance on negative emotion during future-oriented cognition (Marroquín & Nolen-Hoeksema, 2015 ).

While few studies have implemented objective measures of intrinsic motivation in studying anhedonia, recent work links this symptom with difficulties with representations of future states during early stages of motivated behavior (Moutoussis et al., 2018 ). Since intrinsic motivation is driven more by proactive factors as opposed to the more reactive domain of extrinsic motivation, parsing future-oriented decision-making might provide novel insights not only into mechanisms of intrinsic motivation but also anhedonia. When considering the pre-decisional deliberation phase of motivated action ( Fig. 1 ), the representation of a future state may be critical for distinguishing intrinsic v. extrinsic motivation. For example, disrupted representations of intrinsic reinforcers (e.g. autonomy, achievement, task enjoyment, novelty seeking), energy expenditure (Treadway, Cooper, & Miller, 2019 ; Winch, Moberly, & Dickson, 2014 ), or fatigue (Müller, Klein-Flügge, Manohar, Husain, & Apps, 2021 ) might disrupt choice deliberation and interrupt ensuing stages of motivation. This could critically determine the capacity for self-generated, intrinsically-motivated actions (Husain & Roiser, 2018 ). However, relatively few studies have examined this distinction. One study developed a cognitive task that aimed to capture separate measures of self-generated ( intrinsic ) v. externally generated ( extrinsic ) motivation during the option-generation phase (Morris et al., 2020 ). This distinction linked self-generated option generation (intrinsic motivation) to anhedonia symptoms in healthy adults (Morris et al., 2020 ). However, this task still relies on extrinsic rewards, and there is a need for improved tasks that index both behavioral and neural correlates of intrinsic drivers of motivated behavior.

Summary and future directions

In this review, we summarize how intrinsic motivation has been conceptualized, measured, and related to neural function to elucidate its role in psychopathology. In contrast to extrinsic motivation, which has been rapidly incorporated into prominent cognitive, computational, and neurobiological models of human behavior, knowledge of intrinsic motivation remains limited due to evolving conceptualizations, imprecise measurement, and incomplete characterization of its biological correlates. We identify three potential areas of interest for future research.

First, additional objective measures of intrinsically motivation should be developed. This remains challenging experimentally since even the closest approximations of intrinsic motivation (Murayama et al., 2010 ; Rutledge et al., 2017 ) define the construct relative to extrinsic motivation, and other paradigms (e.g. exploration/exploitation tasks) rely on the presence of extrinsic reinforcers. Rather than defining motivated behavior as intrinsic or extrinsic, a more tractable approach might be to consider separate drivers of behavior that can be intrinsic or extrinsic. Future paradigms could index intrinsic motivation by characterizing the effects of intrinsic v. extrinsic reinforcers on motivation for an activity that is enjoyable. Such a design would enable more complex modeling of the effects of distinct reinforcers, and interactions between them, on motivated behavior, which would resolve inconsistencies surrounding the impact of extrinsic reinforcers on intrinsic motivation. For example, monetary incentives might reduce motivation only when a perceived agency is low, or when task enjoyment is high. These interactions might explain paradoxical observations like the undermining effect.

Second, computational models are needed to characterize intrinsic motivation. Computational models of motivation have been successfully implemented in studies of extrinsic motivation, yet few are appropriate for intrinsic motivation due to a focus on action-outcome associations. However, if the intrinsic reward were operationalized as a measurable outcome (e.g. completion of an enjoyable task), reinforcement-learning models could estimate how intrinsic reward value is represented. Advancements in the computational area could significantly improve understanding of the latent processes underlying (ab)normal decision-making, thereby identifying novel therapeutic targets.

Third, although evidence supports the bifurcation of intrinsic and extrinsic motivation at the psychological level, findings at the neural level are more equivocal. Given the overarching role of the mesolimbic dopamine system in learning, reward value estimation, and exploratory behavior, it is perhaps unsurprising that current evidence supports largely overlapping neural circuits for intrinsically and extrinsically motivated behavior. One potential avenue involves targeted pharmacological manipulations or neuromodulation of cortico-limbic circuits to determine if intrinsically and extrinsically motivated behaviors can be systematically modulated in humans. By elucidating the neural circuits of distinct motivational processes and their associations with specific symptom profiles, this approach would improve targeted interventions for highly heterogenous and debilitating disorders like depression.

Financial support

All authors report no financial disclosures. This work was supported by the National Institute of Mental Health (LSM, grant number K01MH120433) and the National Institute on Drug Abuse (MLW, T32DA022975).

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Motivation, the psychological construct ‘invented’ to describe the mechanism by which individuals and groups choose particular behaviour and persist with it, has a history going back millennia in all cultures. Ancient Greek, Roman, Egyptian, Indian, Chinese, and Indigenous cultures from all continents developed rubrics about positively motivated behavior usually under the mantle of ethical behavior and morality (see, for example, Framarin, Citation 2009 ; Hsu & Wu, Citation 2015 ; Pakdel, Citation 2013 ; Reeve, Citation 2015 ). Furthermore, the construct of motivation and how to develop positive motivation and behaviour has permeated all areas of human endeavour. Educational psychology, in particular, has a long history of studying the nature and dynamics of motivation for learning (McInerney, Citation 2015 ). Much of the last 150 years of investigation was dominated by Western theorising and research. And psychologists and educators have learned an enormous amount that has informed educational practice to enhance learning. The last forty or so years has seen a move away from a Western base to theorising and research, a move which now takes culture and human variability as a central tenet for effective research. Rather than looking for regularities and universals with regard to motivation, often powerless in explaining group difference across groups and cultures, and indeed within groups and cultures, more attention is being paid to the culturally specific elements of motivation that may have more efficacy in explaining motivated behaviour in the classroom (King & McInerney, Citation 2016 ; King, McInerney & Pitliya, Citation 2018 ). This issue of Educational Psychology is well situated within this current zeitgeist.

Cheng ( Citation 2019 ) examines the function of extrinsic and intrinsic motivation in Taiwan and the United States. While common beliefs based on historical research in the West posit the superiority of intrinsic motivation for enhancing educational achievement, Cheng found that the dynamics of intrinsic and extrinsic motivation work quite differently in the two cultural settings. We should always ask the question why these constructs (or any others for that matter) should work the same way in quite different cultural milieus rather than, as in the past, expecting them to. Questioning the applicability of Western constructs in non-Western settings should always be paramount.

Hoffman and Kurtz-Costes ( Citation 2019 ) examine an interesting and culturally relevant intervention to enhance the motivation of American Indian children to study science. This research is important because it not only positions an Indigenous group as the focus but utilises a novel methodology that is not constrained by Western protocols. Although the intervention did not appear successful, the robustness of the method and its short duration should be revisited in subsequent research.

Manganelli et al. ( Citation 2019 ) tackle the relationships between self-determined motivation, self-regulated cognitive strategies and prior achievement in predicting academic performance. Italian students participated. The findings are in line with extant research, autonomous motivation and critical thinking are predictive of academic performance, while students with more controlled motivation have lower academic motivation. As exemplified in this research, the transposition of models and theories from North America and their further development from a European perspective allow us to examine the generality of findings and give an impetus for the development of new theoretical perspectives.

In a similar vein Thomas, Cunha, Americo de Souza, and Santo ( Citation 2019 ) examine fairness, trust and school climate in growth mindset among a sample of Brazilian children. The important roles of parental and teacher influence, belief in a just society and various school contextual variables in the development of growth mindset, which are fundamental elements of the research, are tailor-made for cross-cultural testing. The Brazilian context is very relevant for such a study, as the authors state: ‘Brazil is a society of great social inequality; it has many poor and vulnerable groups, yet it is not a poor country. Compared internationally, Brazil has a medium per capita income and plenty of natural resources, yet its distribution is starkly unequal…. For this reason, Brazil is a very relevant yet understudied place to assess perceptions of justice and mindset beliefs’.

Münchow and Bannert ( Citation 2019 ) pick up a theme that has been predominant in European research, that is, the importance of emotions in learning and motivation. Emotions research has, more recently, been impacting North America and international research (see, for example, Crocker et al., Citation 2013 ). The Münchow and Bannert study focuses on positive feelings and the effectiveness of emotional design in enhancing learning. The results suggest that no superiority effect was found with the emotional design. However, it is possible that the small sample and limited design mitigated the potential positive effects. This is a promising line of research to follow-up with stronger research designs.

Using the Big Five Factor Theory of personality, Ljubin-Golub, Petričević, and Rovan ( Citation 2019 ) examine the role of personality in motivational regulation and academic procrastination. Using a Croatian sample, the researchers examined the relations between motivational regulation strategies and procrastination at levels of personality dispositions. In research using North American constructs, as in this study, it is essential to demonstrate methodologically that the research is culturally valid and reliable. Attention is paid in the research to validating the derived questionnaires for the Croatian context. The results demonstrate the importance and usefulness of considering personality characteristics such as conscientiousness, agreeableness and environmental control in understanding academic procrastination.

Collie, Martin, Bobis, Way, and Anderson’s ( Citation 2019 ) study utilises growth modeling to examine aspirations for, or disengagement with, mathematics learning. They gave richness to the study by acknowledging that a complex set of variables are always implicated when considering issues of motivation. In this study, expectancy-value theory, and various socio-educational attributes are included. The findings echo extant research; however, the development of interest or disengagement was impacted by motivation and educational factors. The conclusion highlights that simplistic explanations of developmental trajectories in motivation and learning are inadequate.

In summary, these seven articles present very interesting perspectives on motivation and learning across diverse settings using a range of methodologies. The studies were situated in diverse cultural settings, which is part of the current stream of investigation revisiting theories, research, findings and applications that have been too long dominated by Western protocols. While Western protocols undeniably contribute to the improvement of educational practices internationally, they may be further enhanced by significant attention being paid to differences that characterise groups, and the specific ‘local’ features of motivation and learning that must form the foundation of effective educational practices.

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Motivation: Introduction to the Theory, Concepts, and Research

• First Online: 03 May 2018

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• Paulina Arango 4  

Part of the book series: Literacy Studies ((LITS,volume 15))

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Motivation is a psychological construct that refers to the disposition to act and direct behavior according to a goal. Like most of psychological processes, motivation develops throughout the life span and is influenced by both biological and environmental factors. The aim of this chapter is to summarize research on the development of motivation from infancy to adolescence, which can help understand the typical developmental trajectories of this ability and its relation to learning. We will start with a review of some of the most influential theories of motivation and the aspects each of them has emphasized. We will also explore how biology and experience interact in this development, paying special attention to factors such as: school, family, and peers, as well as characteristics of the child including self-esteem, cognitive development, and temperament. Finally, we will discuss the implications of understanding the developmental trajectories and the factors that have an impact on this development, for both teachers and parents.

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Arango, P. (2018). Motivation: Introduction to the Theory, Concepts, and Research. In: Orellana García, P., Baldwin Lind, P. (eds) Reading Achievement and Motivation in Boys and Girls. Literacy Studies, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-75948-7_1

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