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Response to the critiques (and encouragements) on our critique of motivation constructs

Published online by Cambridge University Press:  31 January 2025

Kou Murayama Open the ORCID record for Kou Murayama [Opens in a new window]  and
Hayley Jach Open the ORCID record for Hayley Jach [Opens in a new window]
Kou Murayama*
Affiliation:
Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Tübingen, Germany [email protected] [email protected] https://motivationsciencelab.com/ Research Institute, Kochi University of Technology, Kochi, Japan
Hayley Jach
Affiliation:
Hector Research Institute of Education Sciences and Psychology, University of Tübingen, Tübingen, Germany [email protected] [email protected] https://motivationsciencelab.com/ Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia
*
*Corresponding author.
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Abstract

The target article argued that motivation constructs are treated as black boxes and called for work that specifies the mental computational processes underlying motivated behavior. In response to critical commentaries, we clarify our philosophical standpoint, elaborate on the meaning of mental computational processes and why past work was not sufficient, and discuss the opportunities to expand the scope of the framework.

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Authors' Response
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Behavioral and Brain Sciences , Volume 48 , 2025 , e50
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Copyright © The Author(s), 2025. Published by Cambridge University Press

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References

Ainslie, G. (2013). Grasping the impalpable: The role of endogenous reward in choices, including process addictions. Inquiry, 56(5), 446–469. https://doi.org/10.1080/0020174X.2013.806129CrossRefGoogle Scholar
Alexander, P. A., Jetton, T. L., & Kulikowich, J. M. (1995). Interrelationship of knowledge, interest, and recall: Assessing a model of domain learning. Journal of Educational Psychology, 87(4), 559–575. https://doi.org/10.1037/0022-0663.87.4.559CrossRefGoogle Scholar
Allen, K., Brändle, F., Botvinick, M., Fan, J. E., Gershman, S. J., Gopnik, A., … Schulz, E. (2024). Using games to understand the mind. Nature Human Behaviour, 8(6), 1035–1043. https://doi.org/10.1038/s41562-024-01878-9CrossRefGoogle ScholarPubMed
Atkinson, J. W. (1957). Motivational determinants of risk-taking behavior. Psychological Review, 64(6, Pt.1), 359–372. https://doi.org/10.1037/h0043445CrossRefGoogle Scholar
Bailey, D. H., Jung, A. J., Beltz, A. M., Eronen, M. I., Gische, C., Hamaker, E. L., … Murayama, K. (2024). Causal inference on human behaviour. Nature Human Behaviour, 8(8), 1448–1459. https://doi.org/10.1038/s41562-024-01939-zCrossRefGoogle ScholarPubMed
Bem, D. J. (1967). Self-perception: An alternative interpretation of cognitive dissonance phenomena. Psychological Review, 74(3), 183–200. https://doi.org/10.1037/h0024835CrossRefGoogle ScholarPubMed
Berridge, K. C. (2023). Separating desire from prediction of outcome value. Trends in Cognitive Sciences, 27(10), 932–946. https://doi.org/10.1016/j.tics.2023.07.007CrossRefGoogle ScholarPubMed
Bindra, D. (1974). A motivational view of learning, performance, and behavior modification. Psychological Review, 81(3), 199–213. https://doi.org/10.1037/h0036330CrossRefGoogle ScholarPubMed
Bloom, B. S. (1976). Human characteristics and school learning. (pp. xii, 284). McGraw-Hill.Google Scholar
Bolles, R. C. (1972). Reinforcement, expectancy, and learning. Psychological Review, 79(5), 394–409. https://doi.org/10.1037/h0033120CrossRefGoogle Scholar
Botvinick, M. M. (2012). Hierarchical reinforcement learning and decision making. Current Opinion in Neurobiology, 22(6), 956–962. https://doi.org/10.1016/j.conb.2012.05.008CrossRefGoogle ScholarPubMed
Brehm, J. W., & Self, E. A. (1989). The intensity of motivation. Annual Review of Psychology, 40, 109–131. https://doi.org/10.1146/annurev.ps.40.020189.000545CrossRefGoogle ScholarPubMed
Bunzeck, N., & Duzel, E. (2006). Absolute coding of stimulus novelty in the human substantia nigra/VTA. Neuron, 51(3), 369–379. https://doi.org/10.1016/j.neuron.2006.06.021CrossRefGoogle ScholarPubMed
Campbell, D. W., & Frey, P. W. (1970). The implications of learning theory for the fade-out of gains from compensatory education. In Hellmuth, J. (Ed.), Compensatory education: A national debate: Vol. 3. Disadvantaged child (pp. 455–463). Brunner/Mazel.Google Scholar
Carroll, J. B. (1963). A model of school learning. Teachers College Record, 64(8), 723–733. https://doi.org/10.1177/016146816306400801CrossRefGoogle Scholar
Crane, T., & Farkas, K. (2022). Mental fact and mental fiction. In Demeter, T., Parent, T., & Toon, A. (Eds.), Mental fictionalism: Philosophical Explorations (pp. 303–319). Routledge.CrossRefGoogle Scholar
Cummins, R. (2000). “How does it work?” versus “what are the laws?”: Two conceptions of psychological explanation. In Keil, F. C. & Wilson, R. A. (Eds.), Explanation and cognition (pp. 117–144). The MIT Press.CrossRefGoogle Scholar
Dayan, P., & Balleine, B. W. (2002). Reward, motivation, and reinforcement learning. Neuron, 36(2), 285–298. https://doi.org/10.1016/S0896-6273(02)00963-7CrossRefGoogle ScholarPubMed
Den Hartigh, R., Cox, R., & van geert, P. (2017). Complex versus complicated models of cognition. In Magnani, L. & Bertolotti, T. (Eds.), Springer handbook of model-based science (pp. 657–669). Springer. https://doi.org/10.1007/978-3-319-30526-4CrossRefGoogle Scholar
Dubey, R., & Griffiths, T. L. (2020). Reconciling novelty and complexity through a rational analysis of curiosity. Psychological Review, 127(3), 455–476. https://doi.org/10.1037/rev0000175CrossRefGoogle ScholarPubMed
Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, 101859. https://doi.org/10.1016/j.cedpsych.2020.101859CrossRefGoogle Scholar
Eronen, M. I. (2020). Causal discovery and the problem of psychological interventions. New Ideas in Psychology, 59, 100785. https://doi.org/10.1016/j.newideapsych.2020.100785CrossRefGoogle Scholar
Fastrich, G. M., & Murayama, K. (2020). Development of interest and role of choice during sequential knowledge acquisition. AERA Open, 6(2), 2332858420929981. https://doi.org/10.1177/2332858420929981CrossRefGoogle Scholar
Fitzgibbon, L., & Murayama, K. (2022). Counterfactual curiosity: Motivated thinking about what might have been. Philosophical Transactions of the Royal Society B: Biological Sciences, 377(1866), 20210340. https://doi.org/10.1098/rstb.2021.0340CrossRefGoogle ScholarPubMed
Fleeson, W., & Jayawickreme, E. (2015). Whole trait theory. Journal of Research in Personality, 56, 82–92. https://doi.org/10.1016/j.jrp.2014.10.009CrossRefGoogle ScholarPubMed
Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1521), 1211–1221. https://doi.org/10.1098/rstb.2008.0300CrossRefGoogle ScholarPubMed
Gernigon, C., Vallacher, R., Nowak, A., & Conroy, D. (2015). Rethinking approach and avoidance in achievement contexts: The perspective of dynamical systems. Review of General Psychology, 19, 443–457.CrossRefGoogle Scholar
Gernigon, C., Den Hartigh, R. J. R., Vallacher, R. R., & van Geert, P. L. C. (2024). How the complexity of psychological processes reframes the issue of reproducibility in psychological science. Perspectives on Psychological Science, 19(6), 952–977. https://doi.org/10.1177/17456916231187324CrossRefGoogle ScholarPubMed
Giron, A. P., Ciranka, S., Schulz, E., van den Bos, W., Ruggeri, A., Meder, B., & Wu, C. M. (2023). Developmental changes in exploration resemble stochastic optimization. Nature Human Behaviour, 7(11), 1955–1967. https://doi.org/10.1038/s41562-023-01662-1CrossRefGoogle ScholarPubMed
Golman, R., Gurney, N., & Loewenstein, G. (2021). Information gaps for risk and ambiguity. Psychological Review, 128(1), 86–103. https://doi.org/10.1037/rev0000252CrossRefGoogle ScholarPubMed
Gottlieb, J., & Oudeyer, P.-Y. (2018). Towards a neuroscience of active sampling and curiosity. Nature Reviews Neuroscience, 19(12), Article 12. https://doi.org/10.1038/s41583-018-0078-0CrossRefGoogle ScholarPubMed
Gruaz, L., Modirshanechi, A., & Brea, J. (2024). Merits of curiosity: A simulation study. OSF. https://doi.org/10.31234/osf.io/evm9nGoogle Scholar
Harnischfeger, A., & Wiley, D. E. (1978). Conceptual issues in models of school learning†. Journal of Curriculum Studies, 10(3), 215–231. https://doi.org/10.1080/0022027780100304CrossRefGoogle Scholar
Hatano, A., Ogulmus, C., Shigemasu, H., & Murayama, K. (2022). Thinking about thinking: People underestimate how enjoyable and engaging just waiting is. Journal of Experimental Psychology: General, 151(2), 3213–3229. https://doi.org/10.1037/xge0001255CrossRefGoogle Scholar
Horstmann, K. T., Rauthmann, J. F., Sherman, R. A., & Ziegler, M. (2020). Unveiling an exclusive link: Predicting behavior with personality, situation perception, and affect in a preregistered experience sampling study. Journal of Personality and Social Psychology, 120(5), 1317–1343. https://doi.org/10.1037/pspp0000357CrossRefGoogle Scholar
Hsiung, A., Poh, J.-H., Huettel, S. A., & Adcock, R. A. (2023). Curiosity evolves as information unfolds. Proceedings of the National Academy of Sciences, 120(43), e2301974120. https://doi.org/10.1073/pnas.2301974120CrossRefGoogle ScholarPubMed
Kaplan, A., & Garner, J. K. (2017). A complex dynamic systems perspective on identity and its development: The dynamic systems model of role identity. Developmental Psychology, 53(11), 2036–2051. https://doi.org/10.1037/dev0000339CrossRefGoogle ScholarPubMed
Kim, S., Sakaki, M., & Murayama, K. (2024). Metacognition of curiosity: People underestimate the seductive lure of non-instrumental information. Psychonomic Bulletin & Review, 31(3), 1–12. https://doi.org/10.3758/s13423-023-02404-0CrossRefGoogle ScholarPubMed
Kobayashi, K., Ravaioli, S., Baranès, A., Woodford, M., & Gottlieb, J. (2019). Diverse motives for human curiosity. Nature Human Behaviour, 3(6), 587–595. https://doi.org/10.1038/s41562-019-0589-3CrossRefGoogle ScholarPubMed
Kruglanski, A. W., Bélanger, J. J., Chen, X., Köpetz, C., Pierro, A., & Mannetti, L. (2012). The energetics of motivated cognition: A force-field analysis. Psychological Review, 119(1), 1–20. https://doi.org/10.1037/a0025488CrossRefGoogle ScholarPubMed
Kuper, N., Breil, S. M., Horstmann, K. T., Roemer, L., Lischetzke, T., Sherman, R. A., … Rauthmann, J. F. (2022). Individual differences in contingencies between situation characteristics and personality states. Journal of Personality and Social Psychology, 123(5), 1166–1198. https://doi.org/10.1037/pspp0000435CrossRefGoogle ScholarPubMed
Kuratomi, K., Johnsen, L., Kitagami, S., Hatano, A., & Murayama, K. (2023). People underestimate their capability to motivate themselves without performance-based extrinsic incentives. Motivation and Emotion, 47, 509–523. https://doi.org/10.1007/s11031-022-09996-5CrossRefGoogle ScholarPubMed
Laskar, P., & van der Maas, H. L. (2024). A reciprocal-practice-success (RPS) model of free practice. Proceedings of the Annual Meeting of the Cognitive Science Society, 46, 2818–2824. https://escholarship.org/uc/item/98s6q1bcGoogle Scholar
Lewin, K. (1942). Field theory and learning. Teachers College Record, 43(10), 215–242. https://doi.org/10.1177/016146814204301006CrossRefGoogle Scholar
Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman and Company.Google Scholar
Meliss, S., Tsuchiyagaito, A., Byrne, P., van Reekum, C., & Murayama, K. (2024). Broad brain networks support curiosity-motivated incidental learning of naturalistic dynamic stimuli with and without monetary incentives. Imaging Neuroscience, 2, 1–27. https://doi.org/10.1162/imag_a_00134CrossRefGoogle Scholar
Modirshanechi, A., Kondrakiewicz, K., Gerstner, W., & Haesler, S. (2023a). Curiosity-driven exploration: Foundations in neuroscience and computational modeling. Trends in Neurosciences, 46(12), 1054–1066. https://doi.org/10.1016/j.tins.2023.10.002CrossRefGoogle ScholarPubMed
Modirshanechi, A., Lin, W.-H., Xu, H. A., Herzog, M. H., & Gerstner, W. (2023b). The curse of optimism: A persistent distraction by novelty (p. 2022.07.05.498835). bioRxiv. https://doi.org/10.1101/2022.07.05.498835CrossRefGoogle Scholar
Murayama, K. (2022a). A reward-learning framework of knowledge acquisition: An integrated account of curiosity, interest, and intrinsic–extrinsic rewards. Psychological Review, 129(1), 175–198. https://doi.org/10.1037/rev0000349CrossRefGoogle ScholarPubMed
Murayama, K. (2022b). Are cognition, motivation, and emotion the same or different?: Let's abandon that thinking. In Bong, M., Kim, S., & Reeve, J. (Eds.), Motivation science: Controversies and insights (pp. 243–245). Oxford University Press.Google Scholar
Murayama, K., & Jach, H. (2024). A critique of motivation constructs to explain higher-order behavior: We should unpack the black box. Behavioral and Brain Sciences, 1–53. https://doi.org/10.1017/S0140525X24000025CrossRefGoogle ScholarPubMed
Murayama, K., Kitagami, S., Tanaka, A., & Raw, J. A. L. (2016). People's naiveté about how extrinsic rewards influence intrinsic motivation. Motivation Science, 2(3), 138–142. https://doi.org/10.1037/mot0000040CrossRefGoogle Scholar
Murayama, K., FitzGibbon, L., & Sakaki, M. (2019). Process account of curiosity and interest: A reward-learning perspective. Educational Psychology Review, 31(4), 875–895. https://doi.org/10.1007/s10648-019-09499-9CrossRefGoogle Scholar
Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84(3), 231–259. https://doi.org/10.1037/0033-295X.84.3.231CrossRefGoogle Scholar
Noordewier, M. K., & van Dijk, E. (2015). Curiosity and time: From not knowing to almost knowing. Cognition and Emotion, 31(3), 411–421. https://doi.org/10.1080/02699931.2015.1122577CrossRefGoogle Scholar
Pessoa, L. (2017). A network model of the emotional brain. Trends in Cognitive Sciences, 21(5), 357–371. https://doi.org/10.1016/j.tics.2017.03.002CrossRefGoogle ScholarPubMed
Pintrich, P. R., & Schunk, D. H. (2002). Motivation in education: Theory, research, and applications (2nd ed.). Merrill-Prentice Hall.Google Scholar
Poli, F., O'Reilly, J. X., Mars, R. B., & Hunnius, S. (2024). Curiosity and the dynamics of optimal exploration. Trends in Cognitive Sciences, 28(5), 441–453. https://doi.org/10.1016/j.tics.2024.02.001CrossRefGoogle ScholarPubMed
Reber, R., Winkielman, P., & Schwarz, N. (1998). Effects of perceptual fluency on affective judgments. Psychological Science, 9(1), 45–48. https://doi.org/10.1111/1467-9280.00008CrossRefGoogle Scholar
Richter, M., Gendolla, G. H. E., & Wright, R. A. (2016). Three decades of research on motivational intensity theory: What we have learned about effort and what we still don't know. Advances in Motivation Science, 3, 149–186.CrossRefGoogle Scholar
Roemer, L., Horstmann, K. T., & Ziegler, M. (2021). Sometimes hot, sometimes not: The relations between selected situational vocational interests and situation perception. European Journal of Personality, 35(2), 212–233. https://doi.org/10.1002/per.2287CrossRefGoogle Scholar
Sawyer, R. K. (2002). Emergence in psychology: Lessons from the history of non-reductionist science. Human Development, 45(1), 2–28. https://doi.org/10.1159/000048148CrossRefGoogle Scholar
Shenhav, A. (2024). The affective gradient hypothesis: An affect-centered account of motivated behavior. Trends in Cognitive Sciences, 28(12), 1089–1104. https://doi.org/10.1016/j.tics.2024.08.003CrossRefGoogle ScholarPubMed
Silvestrini, N., Musslick, S., Berry, A. S., & Vassena, E. (2023). An integrative effort: Bridging motivational intensity theory and recent neurocomputational and neuronal models of effort and control allocation. Psychological Review, 130(4), 1081–1103. https://doi.org/10.1037/rev0000372CrossRefGoogle ScholarPubMed
Singh, A., & Murayama, K. (2024). Creativity is motivated by novelty. Curiosity is triggered by uncertainty. Behavioral and Brain Sciences, 47, e115. https://doi.org/10.1017/S0140525X23003291CrossRefGoogle ScholarPubMed
Sizemore, A. E., Karuza, E. A., Giusti, C., & Bassett, D. S. (2018). Knowledge gaps in the early growth of semantic feature networks. Nature Human Behaviour, 2(9), 682–692. https://doi.org/10.1038/s41562-018-0422-4CrossRefGoogle ScholarPubMed
Ten, A., Oudeyer, P.-Y., Sakaki, M., & Murayama, K. (2024). The curious U: Integrating theories linking knowledge and information-seeking behavior. OSF. https://doi.org/10.31234/osf.io/s8mkjGoogle Scholar
Toates, F. M. (1986). Motivational systems. Cambridge University Press.Google Scholar
Toon, A., & Toon, A. (2023). Mind as metaphor: A defence of mental fictionalism. Oxford University Press.CrossRefGoogle Scholar
van der Maas, H. L. J. (2024). Complex-systems research in psychology. SFI Press.CrossRefGoogle Scholar
Van Orden, G. C., Holden, J. G., & Turvey, M. T. (2003). Self-organization of cognitive performance. Journal of Experimental Psychology: General, 132(3), 331–350. https://doi.org/10.1037/0096-3445.132.3.331CrossRefGoogle ScholarPubMed
van Rooij, I., & Baggio, G. (2021). Theory before the test: How to build high-verisimilitude explanatory theories in psychological science. Perspectives on Psychological Science, 16(4), 682–697. https://doi.org/10.1177/1745691620970604CrossRefGoogle ScholarPubMed
Vroom, V. H. (1964). Work and motivation. Wiley.Google Scholar
Weiner, B. (1991). Metaphors in motivation and attribution. American Psychologist, 46(9), 921–930.CrossRefGoogle Scholar
Witherby, A. E., & Carpenter, S. K. (2022). The rich-get-richer effect: Prior knowledge predicts new learning of domain-relevant information. Journal of Experimental Psychology: Learning, Memory, and Cognition, 48(4), 483–498. https://doi.org/10.1037/xlm0000996Google ScholarPubMed
Yan, V. X., Sana, F., & Carvalho, P. F. (2024). No simple solutions to Complex problems: Cognitive science principles can guide but not prescribe educational decisions. Policy Insights from the Behavioral and Brain Sciences, 11(1), 59–66. https://doi.org/10.1177/23727322231218906CrossRefGoogle Scholar