The DOI matches its Crossref record; 65 of the 68 references it cites were found in a registry or an index, and 3 are not checked yet.
Humans primarily use model-based inference in the two-stage task
Open accessPublished version of a preprint
Abstract
Distinct model-free and model-based learning processes are thought to drive both typical and dysfunctional behaviours. Data from two-stage decision tasks have seemingly shown that human behaviour is driven by both processes operating in parallel. However, in this study, we show that more detailed task instructions lead participants to make primarily model-based choices that have little, if any, simple model-free influence. We also demonstrate that behaviour in the two-stage task may falsely appear to be driven by a combination of simple model-free and model-based learning if purely model-based agents form inaccurate models of the task because of misconceptions. Furthermore, we report evidence that many participants do misconceive the task in important ways. Overall, we argue that humans formulate a wide variety of learning models. Consequently, the simple dichotomy of model-free versus model-based learning is inadequate to explain behaviour in the two-stage task and connections between reward learning, habit formation and compulsivity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Stan : A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754
Statsmodels: Econometric and Statistical Modeling with Python
Seabold, S., & Perktold, J. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-011 · Cited by 6,968
(2015) · doi:10.1007/s11222-016-9696-4 · Cited by 5,611
Daw, N. D., Niv, Y., & Dayan, P. (2005) · Nature Neuroscience · doi:10.1038/nn1560 · Cited by 2,533
A framework for studying the neurobiology of value-based decision making
Rangel, A., Camerer, C., & Montague, P. R. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2357 · Cited by 2,184
Model-based influences on humans’ choices and striatal prediction errors
Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P., & Dolan, R. J. (2011) · Neuron · doi:10.1016/j.neuron.2011.02.027 · Cited by 1,779
Balleine, B. W., & O'Doherty, J. P. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2009.131 · Cited by 1,775
Visual fixations and the computation and comparison of value in simple choice
Krajbich, I., Armel, C., & Rangel, A. (2010) · Nature Neuroscience · doi:10.1038/nn.2635 · Cited by 1,408
Research works printed but not linked: 2
- Miller, K. J., Shenhav, A. & Ludvig, E. A. Habits without values. Psychol. Rev. 126, 292–311 (2019). · DOI · Paper page
- Economides, M., Kurth-Nelson, Z., Lübbert, A., Guitart-Masip, M. & Dolan, R. J. Model-based reasoning in humans becomes automatic with training. PLoS Comput. Biol. 11, e1004463 (2015). · DOI
Cited by
Sorted by citations
Using cognitive psychology to understand GPT-3
Binz, M., & Schulz, E. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2218523120 · Cited by 559
Reinforcement-learning in fronto-striatal circuits
Averbeck, B. B., & O’Doherty, J. P. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01108-0 · Cited by 132
A foundation model to predict and capture human cognition
Binz, M., Akata, E., Bethge, M., Brändle, F., Callaway, F., Coda-Forno, J., Dayan, P., Demircan, C., Eckstein, M. K., Éltető, N., et al. (2025) · Nature · doi:10.1038/s41586-025-09215-4 · Cited by 119
The interpretation of computational model parameters depends on the context
Eckstein, M. K., Master, S. L., Xia, L., Dahl, R. E., Wilbrecht, L., & Collins, A. G. E. (2022) · eLife · doi:10.7554/elife.75474 · Cited by 124
Vandaele, Y., & Ahmed, S. H. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00899-y · Cited by 107
Functional neuroimaging in psychiatry and the case for failing better
Nour, M. M., Liu, Y., & Dolan, R. J. (2022) · Neuron · doi:10.1016/j.neuron.2022.07.005 · Cited by 92
Decision-making ability, psychopathology, and brain connectivity
Moutoussis, M., Garzón, B., Neufeld, S., Bach, D. R., Rigoli, F., Goodyer, I. M., Bullmore, E. T., Fonagy, P., Jones, P. B., Hauser, T. U., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.04.019 · Cited by 88
Why and how the brain weights contributions from a mixture of experts
O’Doherty, J. P., Lee, S. W., Tadayonnejad, R., Cockburn, J., Iigaya, K., & Charpentier, C. J. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.10.022 · Cited by 70
Discovering cognitive strategies with tiny recurrent neural networks
Ji-An, L., Benna, M. K., & Mattar, M. G. (2025) · Nature · doi:10.1038/s41586-025-09142-4 · Cited by 64
Model-free decision making is prioritized when learning to avoid harming others
Lockwood, P. L., Klein-Flügge, M. C., Abdurahman, A., & Crockett, M. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2010890117 · Cited by 67
The neuroscience of active learning and direct instruction
Dubinsky, J. M., & Hamid, A. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105737 · Cited by 121
A taxonomy of surprise definitions
Modirshanechi, A., Brea, J., & Wulfram Gerstner, B. (2022) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2022.102712 · Cited by 67
Seow, T. X. F., Benoit, E., Dempsey, C., Jennings, M., Maxwell, A., O’Connell, R. G., & Gillan, C. M. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0031-21.2021 · Cited by 52
Naturalistic reinforcement learning
Wise, T., Emery, K. J., & Radulescu, A. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.08.016 · Cited by 55
Explicit knowledge of task structure is a primary determinant of human model-based action
Castro-Rodrigues, P., Akam, T., Snorasson, I., Camacho, M., Paixão, V., Maia, A., Barahona-Corrêa, B., Dayan, P., Simpson, H. B., Costa, R. M., et al. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01346-2 · Cited by 50
Chen, H., Mojtahedzadeh, N., Belanger, M. J., Nebe, S., Kuitunen‐Paul, S., Sebold, M., Garbusow, M., Huys, Q. J. M., Heinz, A., Rapp, M. A., et al. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.01.009 · Cited by 42
Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges
Dennison, J. B., Sazhin, D., & Smith, D. V. (2022) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1589 · Cited by 50
Humans can navigate complex graph structures acquired during latent learning
Rmus, M., Ritz, H., Hunter, L. E., Bornstein, A. M., & Shenhav, A. (2019) · Cognition · doi:10.1016/j.cognition.2022.105103 · Cited by 42
Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making
He, X., Modirshanechi, A., Lehmann, M. P., Wulfram Gerstner, B., & Herzog, M. H. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009070 · Cited by 49
Distinct replay signatures for prospective decision-making and memory preservation
Wimmer, G. E., Liu, Y., McNamee, D., & Dolan, R. J. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2205211120 · Cited by 47
Goal-Directed and Habitual Control in Human Substance Use: State of the Art and Future Directions
Doñamayor, N., Ebrahimi, C., Arndt, V. A., Weiß, F., Schlagenhauf, F., & Endraß, T. (2022) · Neuropsychobiology · doi:10.1159/000527663 · Cited by 40
Disrupted state transition learning as a computational marker of compulsivity
Sharp, P. B., Dolan, R. J., & Eldar, E. (2021) · Psychological Medicine · doi:10.1017/s0033291721003846 · Cited by 30
Rethinking model-based and model-free influences on mental effort and striatal prediction errors
da Silva, C. F., Lombardi, G., Edelson, M. G., & Hare, T. A. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01573-1 · Cited by 39
Self-citationRecent developments in the habit hypothesis of OCD and compulsive disorders
Gillan, C. M. (2021) · Current Topics in Behavioral Neurosciences · doi:10.1007/7854_2020_199 · Cited by 27
Wagner, B., Mathar, D., & Peters, J. (2022) · Computational Psychiatry · doi:10.5334/cpsy.84 · Cited by 25
Valence bias in metacontrol of decision making in adolescents and young adults
Bolenz, F., & Eppinger, B. (2021) · Child Development · doi:10.1111/cdev.13693 · Cited by 27
Buabang, E. K., Boddez, Y., Houwer, J. D., & Moors, A. (2021) · Motivation Science · doi:10.1037/mot0000218 · Cited by 24
Distinct value computations support rapid sequential decisions
Mah, A., Schiereck, S. S., Bossio, V., & Constantinople, C. M. (2023) · Nature Communications · doi:10.1038/s41467-023-43250-x · Cited by 25
Negative symptoms, striatal dopamine and model-free reward decision-making in schizophrenia
Brandl, F., Knolle, F., Avram, M., Leucht, C., Yakushev, I., Priller, J., Leucht, S., Ziegler, S., Wunderlich, K., & Sorg, C. (2023) · Brain · doi:10.1093/brain/awac268 · Cited by 24
Latent-state and model-based learning in PTSD
Cisler, J. M., Dunsmoor, J. E., Fonzo, G. A., & Nemeroff, C. B. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2023.12.002 · Cited by 26
Shahar, N., Hauser, T. U., Moran, R., Moutoussis, M., Principal investigators, Bullmore, E. T., Dolan, R. J., Goodyer, I. M., Fonagy, P., Jones, P. B., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01642-x · Cited by 25
Summerfield, C. (2022) · doi:10.1093/oso/9780192843883.001.0001 · Cited by 24
Hartogsveld, B., Quaedflieg, C. W. E. M., van Ruitenbeek, P., & Smeets, T. (2021) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2021.105596 · Cited by 21
Grujic, N., Brus, J., Burdakov, D., & Polanía, R. (2022) · Science Advances · doi:10.1126/sciadv.abj8935 · Cited by 22
Moughrabi, N., Botsford, C., Gruichich, T. S., Azar, A., Heilicher, M. O., Hiser, J., Crombie, K. M., Dunsmoor, J. E., Stowe, Z. N., & Cisler, J. M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119709 · Cited by 22
Hackel, L. M., Kalkstein, D. A., & Mende‐Siedlecki, P. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.004 · Cited by 20
Individual differences in learning positive affective value
Wuensch, L., Pool, E. R., & Sander, D. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.11.001 · Cited by 18
Part-Time Bayesians: Incentives and Behavioral Heterogeneity in Belief Updating
Alós‐Ferrer, C., & Garagnani, M. (2023) · Management Science · doi:10.1287/mnsc.2022.4584 · Cited by 17
Mikuš, N., Korb, S., Massaccesi, C., Gausterer, C., Graf, I., Willeit, M., Eisenegger, C., Lamm, C., Silani, G., & Mathys, C. D. (2022) · eLife · doi:10.7554/elife.79661 · Cited by 14
Hunger improves reinforcement-driven but not planned action
van Swieten, M. M. H., Bogacz, R., & Manohar, S. (2021) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-021-00921-w · Cited by 17
Goal-directed and habitual decision making under stress in gambling disorder: An fMRI study
van Timmeren, T., Piray, P., Goudriaan, A. E., & van Holst, R. J. (2023) · Addictive Behaviors · doi:10.1016/j.addbeh.2023.107628 · Cited by 18
Effects of subclinical depression on prefrontal–striatal model-based and model-free learning
Heo, S., Sung, Y., & Lee, S. W. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009003 · Cited by 16
Nissan, N., Hertz, U., Shahar, N., & Gabay, Y. (2023) · Behavioral and Brain Functions · doi:10.1186/s12993-023-00207-w · Cited by 18
Using smartphones to optimise and scale-up the assessment of model-based planning
Donegan, K. R., Brown, V. M., Price, R. B., Gallagher, E., Pringle, A. J., Hanlon, A. K., & Gillan, C. M. (2023) · Communications Psychology · doi:10.1038/s44271-023-00031-y · Cited by 16
Is the habit system altered in individuals with obesity? A systematic review
Ciria, L. F., Watson, P., Vadillo, M. A., & Luque, D. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.07.006 · Cited by 18
Tracking the contribution of inductive bias to individualised internal models
Török, B., Nagy, D., Kiss, M. M., Janacsek, K., Németh, D., & Orbán, G. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010182 · Cited by 17
Active inference and the two-step task
Gijsen, S., Grundei, M., & Blankenburg, F. (2022) · Scientific Reports · doi:10.1038/s41598-022-21766-4 · Cited by 16
Ruan, Z., Seger, C. A., Yang, Q., Kim, D., Lee, S. W., Chen, Q., & Peng, Z. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1162800 · Cited by 15
Neurocomputational model of compulsivity: deviating from an uncertain goal-directed system
Kim, T., Lee, S. W., Lho, S. K., Moon, S., Kim, M., & Kwon, J. S. (2024) · Brain · doi:10.1093/brain/awae102 · Cited by 15
A practical guide for studying human behavior in the lab
Barbosa, J., Stein, H., Zorowitz, S., Niv, Y., Summerfield, C., Soto‐Faraco, S., & Hyafil, A. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01793-9 · Cited by 14
Spontaneous mind wandering impairs model-based decision making
Liu, S., Rabovsky, M., & Schad, D. J. (2023) · PLoS ONE · doi:10.1371/journal.pone.0279532 · Cited by 15
Neural correlates of model-based behavior in internet gaming disorder and alcohol use disorder
Kwon, M., Choi, H., Park, H., Ahn, W., & Jung, Y. (2024) · Journal of Behavioral Addictions · doi:10.1556/2006.2024.00006 · Cited by 13
Liakoni, V., Lehmann, M. P., Modirshanechi, A., Brea, J., Lutti, A., Wulfram Gerstner, B., & Preuschoff, K. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118780 · Cited by 16
Impaired belief updating and devaluation in adult women with bulimia nervosa
Berner, L. A., Fiore, V. G., Chen, J. Y., Krueger, A., Kaye, W. H., Viranda, T., & de Wit, S. (2023) · Translational Psychiatry · doi:10.1038/s41398-022-02257-6 · Cited by 18
Hiser, J., Heilicher, M. O., Botsford, C., Crombie, K. M., Bellani, J., Azar, A., Fonzo, G. A., Nacewicz, B. M., & Cisler, J. M. (2023) · Behaviour Research and Therapy · doi:10.1016/j.brat.2023.104361 · Cited by 16
Novelty as a drive of human exploration in complex stochastic environments
Modirshanechi, A., Lin, W.-H., He, X., Herzog, M. H., & Wulfram Gerstner, B. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2502193122 · Cited by 19
Social Concepts Simplify Complex Reinforcement Learning
Hackel, L. M., & Kalkstein, D. A. (2023) · Psychological Science · doi:10.1177/09567976231180587 · Cited by 11
Understanding dual process cognition via the minimum description length principle
Moskovitz, T., Miller, K. J., Sahani, M., & Botvinick, M. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012383 · Cited by 21
The comparable strategic flexibility of model-free and model-based learningPreprint
Filipowicz, A. L. S., Levine, J., Piasini, E., Tavoni, G., Kable, J. W., & Gold, J. I. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.28.879965 · Cited by 10
Knolle, F., Sen, P., Culbreth, A., Koch, K., Schmitz-Koep, B., Gürsel, D. A., Wunderlich, K., Avram, M., Berberich, G., Sorg, C., et al. (2024) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.240020 · Cited by 9
Hogarth, L. (2024) · Addiction Biology · doi:10.1111/adb.13435 · Cited by 8
Imbalanced goal-directed and habitual control in individuals with internet gaming disorder
Lei, W., Huang, Y., Peng, Y., Chen, G., Gong, K., Liu, K., Wu, D., Qin, C., Cheng, X., Tian, M., et al. (2025) · Journal of Behavioral Addictions · doi:10.1556/2006.2025.00037 · Cited by 11
Inefficient prioritization of task-relevant attributes during instrumental information demand
Rischall, I., Hunter, L., Jensen, G., & Gottlieb, J. P. (2023) · Nature Communications · doi:10.1038/s41467-023-38821-x · Cited by 9
Colas, J. T., Dundon, N. M., Gerraty, R. T., Saragosa‐Harris, N. M., Szymula, K. P., Tanwisuth, K., Tyszka, J. M., van Geen, C., Ju, H., Toga, A. W., et al. (2022) · Human Brain Mapping · doi:10.1002/hbm.25988 · Cited by 10
Bayramoğlu, G., & Öztürk, O. (2024) · Journal of Economic Surveys · doi:10.1111/joes.12676 · Cited by 8
Signatures of heuristic-based directed exploration in two-step sequential decision task behaviour
Brands, A. M. A., Mathar, D., & Peters, J. (2025) · Computational Psychiatry · doi:10.5334/cpsy.101 · Cited by 6
Crombie, K. M., Azar, A., Botsford, C., Heilicher, M. O., Hiser, J., Moughrabi, N., Gruichich, T. S., Schomaker, C. M., & Cisler, J. M. (2023) · Journal of Mood and Anxiety Disorders · doi:10.1016/j.xjmad.2023.100015 · Cited by 6
How are overweight and obesity associated with reinforcement learning deficits? A systematic review
Weydmann, G. J., Miguel, P. M., Hakim, N., Dubé, L., Silveira, P. P., & Bizarro, L. (2023) · Appetite · doi:10.1016/j.appet.2023.107123 · Cited by 6
Defining and Measuring Habits Across Different Fields of Research
Watson, P. (2024) · Springer eBooks · doi:10.1007/978-3-031-55889-4_1 · Cited by 5
Novelty is not Surprise: Human exploratory and adaptive behavior in sequential decision-makingPreprint
He, X., Modirshanechi, A., Lehmann, M. P., Wulfram Gerstner, B., & Herzog, M. H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.24.311084 · Cited by 7
Does the Habit Theory of Addictions Extend to Disordered Gambling?
van Timmeren, T., & Clark, L. (2026) · Current Addiction Reports · doi:10.1007/s40429-026-00715-5 · Cited by 4
Shorter planning depth and higher response noise during sequential decision-making in old age
Steffen, J., Marković, D., Glöckner, F., Neukam, P. T., Kiebel, S. J., Li, S., & Smolka, M. N. (2023) · Scientific Reports · doi:10.1038/s41598-023-33274-0 · Cited by 6
Yang, L., Jin, F., Yang, L., Li, J., Zhi-hui, L., Li, M., & Shang, Z. (2024) · Animals · doi:10.3390/ani14030431 · Cited by 4
Working Memory Guides Action Valuation in Model-based Decision-making Strategy
Zuo, Z., Yang, L., Wang, H., & Li, H. (2024) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02237 · Cited by 5
Looking deeper into the algorithms underlying human planning
Kuperwajs, I., Russek, E. M., Mattar, M. G., Ma, W. J., & Griffiths, T. L. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2025.06.006 · Cited by 5
Validating Habitual and Goal-Directed Decision-Making Performance Online in Healthy Older Adults
Ito, K. L., Cao, L., Reinberg, R., Keller, B., Monterosso, J. R., Schweighofer, N., & Liew, S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.702810 · Cited by 4
The Component Processes of Complex Planning Follow Distinct Developmental Trajectories
Ma, I., Phaneuf-Hadd, C. V., van Opheusden, B., Ma, W. J., & Hartley, C. A. (2023) · Conference on Cognitive Computational Neuroscience (CCN) · doi:10.32470/ccn.2023.1330-0 · Cited by 4
Contextual overtraining accelerates habit formation in new stimuliPreprint
Lesage, E., & Verguts, T. (2021) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/7m6bh · Cited by 4
Multi-step inference can be improved across the lifespan with individualized memory interventionsPreprint
Noh, S. M., Cooper, K. W., Guo, S., Zhou, D., Stark, C. E. L., & Bornstein, A. M. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/3mhj6 · Cited by 6
Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learningPreprint
Venditto, S. J. C., Miller, K. J., Brody, C. D., & Daw, N. D. (2024) · eLife · doi:10.7554/elife.97612.2 · Cited by 4
Luna, R., Vadillo, M. A., & Luque, D. (2023) · Cortex · doi:10.1016/j.cortex.2023.06.009 · Cited by 3
Noradrenergic and Dopaminergic modulation of meta-cognition and meta-control
Ershadmanesh, S., Rajabi, S., Rostami, R., Moran, R., & Dayan, P. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012675 · Cited by 3
Memory for rewards guides retrieval
Nagel, J., Morgan, D. P., Gürsoy, Ç. N., Sander, S., Kern, S., & Feld, G. B. (2024) · Communications Psychology · doi:10.1038/s44271-024-00074-9 · Cited by 3
Hong, Z. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3524882 · Cited by 3
Evidence for shallow cognitive maps in schizophrenia
Karagoz, A. B., Moran, E. K., Barch, D. M., Kool, W., & Reagh, Z. M. (2025) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-025-01283-3 · Cited by 5
Critical intelligence: Computing defensive behaviour
Brochard, J., Dayan, P., & Bach, D. R. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106213 · Cited by 3
Disorders of compulsivity: Deficits in arbitrating learning strategies
Ruan, Z., Liu, S., Liu, Y. A., Yang, Q., & Peng, Z. (2024) · Addiction Biology · doi:10.1111/adb.13433 · Cited by 3
Distinct replay signatures for prospective decision-making and memory preservationPreprint
Wimmer, G. E., Liu, Y., McNamee, D., & Dolan, R. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.08.467745 · Cited by 3
Inefficient Prioritization of Task-Relevant Attributes During Instrumental Information DemandPreprint
Rischall, I., Hunter, L., Jensen, G., & Gottlieb, J. P. (2022) · doi:10.31234/osf.io/vsqxk · Cited by 3
The construction and use of cognitive maps in model-based controlPreprint
Karagoz, A. B., Reagh, Z. M., & Kool, W. (2022) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/ngqwa · Cited by 3
Hamroun, S., Lebreton, M. P., & Palminteri, S. (2022) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/2uw85 · Cited by 3
Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learningPreprint
Venditto, S. J. C., Miller, K. J., Brody, C. D., & Daw, N. D. (2024) · eLife · doi:10.7554/elife.97612.1 · Cited by 3
A computational principle of habit formationPreprint
Lakshminarasimhan, K. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.12.618033 · Cited by 3
Model-free decision-making underlies motor errors in rapid sequential movements under threat
Sankhe, P., & Haruno, M. (2024) · Communications Psychology · doi:10.1038/s44271-024-00123-3 · Cited by 2
Model-based algorithms shape automatic evaluative processing
Melnikoff, D. E., & Kurdi, B. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2417068122 · Cited by 2
Kastner, D. B., Yokota, N. O., Lee, C. Y., Holobetz, C., Chaudhry, A., Williams, G., Ton, J. N., Mehra, M., Kharazia, V., Parkinson, D. Y., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-76681-3 · Cited by 2
Integrating neuro-psychological habit research into consumer choice models
Webb, R., Fong, J., Landry, P., Levine, J., Wellsjo, A., Natan, O., Mazar, A., Zhao, C., Lally, P. J., de Wit, S., et al. (2025) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2025.10.002 · Cited by 2
Ebrahimi, C., Musial, M. P. M., Doñamayor, N., Prychynenko, D. S., Bode, E. L., Spanagel, R., Heinz, A., Deserno, L., Smolka, M. N., Ebner‐Priemer, U., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03660-5 · Cited by 3
A web-based implementation of the two-step human reinforcement learning taskPreprint
Kinley, I. (2023) · doi:10.31234/osf.io/b5x3w · Cited by 2
Maturation of striatal dopamine supports the development of habitual behavior through adolescencePreprint
Petrie, D. J., Parr, A. C., Sydnor, V. J., Ojha, A., Foran, W., Tervo‐Clemmens, B., Calabro, F. J., & Luna, B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.06.631527 · Cited by 3
Metadata
- Authors
- Feher da Silva, C., & Hare, T. A.
- Affiliations
- University of Zurich (CH)
- Corresponding author
- Hare, Todd A.
- Venue
- Nature Human Behaviour
- Published
- 2020-07-06
- Volume
- 4
- Issue
- 10
- Pages
- 1053-1066
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W3040440677
- Cited by
- 153
- References
- 64
- Citations by year
- 2026: 13 · 2025: 30 · 2024: 25 · 2023: 26 · 2022: 32 · 2021: 20 · 2020: 6
- FWCI
- 11.30
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Repository@Nottingham (University of Nottingham) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- model-based learning, model-free learning, task instructions, misconceptions, reward learning, habit formation, compulsivity, learning models, decision-making
- MeSH terms
- Adult, Choice Behavior, Goals, Habits, Humans, Models, Psychological, Probability Learning, Reinforcement, Psychology, Reward, Task Performance and Analysis, Thinking, Models, Statistical, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
