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  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., et al. (2022). Explicit knowledge of task structure is a primary determinant of human model-based action. Nature Human Behaviour, Epub ahead. doi:10.1038/s41562-022-01346-2.

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Castro-Rodrigues, P, Author
Akam, T, Author
Snorasson, I, Author
Camacho, M, Author
Paixão, V, Author
Maia, A, Author
Barahona-Corrêa, JB, Author
Dayan, P1, Author           
Simpson, HB, Author
Costa, RM, Author
Oliveira-Maia, AJ, Author
Affiliations:
1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              

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 Abstract: Explicit information obtained through instruction profoundly shapes human choice behaviour. However, this has been studied in computationally simple tasks, and it is unknown how model-based and model-free systems, respectively generating goal-directed and habitual actions, are affected by the absence or presence of instructions. We assessed behaviour in a variant of a computationally more complex decision-making task, before and after providing information about task structure, both in healthy volunteers and in individuals suffering from obsessive-compulsive or other disorders. Initial behaviour was model-free, with rewards directly reinforcing preceding actions. Model-based control, employing predictions of states resulting from each action, emerged with experience in a minority of participants, and less in those with obsessive-compulsive disorder. Providing task structure information strongly increased model-based control, similarly across all groups. Thus, in humans, explicit task structural knowledge is a primary determinant of model-based reinforcement learning and is most readily acquired from instruction rather than experience.

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 Dates: 2022-05
 Publication Status: Published online
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 Rev. Type: -
 Identifiers: DOI: 10.1038/s41562-022-01346-2
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Title: Nature Human Behaviour
Source Genre: Journal
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Publ. Info: London : Nature Research
Pages: - Volume / Issue: Epub ahead Sequence Number: - Start / End Page: - Identifier: ISSN: 2397-3374
CoNE: https://pure.mpg.de/cone/journals/resource/2397-3374