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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.
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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
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Averbeck, B. B., & O’Doherty, J. P. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01108-0 · Cited by 132
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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
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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
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