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  Models that learn how humans learn: The case of decision-making and its disorders

Dezfouli, A., Griffiths, K., Ramos, F., Dayan, P., & Balleine, B. (2019). Models that learn how humans learn: The case of decision-making and its disorders. PLoS Computational Biology, 16(6), 1-33. doi:10.1371/journal.pcbi.1006903.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-C3ED-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-D672-B
Genre: Journal Article

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Dezfouli , A, Author
Griffiths, K, Author
Ramos, F, Author
Dayan, P1, 2, Author              
Balleine, BW, Author
Affiliations:
1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Popular computational models of decision-making make specific assumptions about learning processes that may cause them to underfit observed behaviours. Here we suggest an alternative method using recurrent neural networks (RNNs) to generate a flexible family of models that have sufficient capacity to represent the complex learning and decision- making strategies used by humans. In this approach, an RNN is trained to predict the next action that a subject will take in a decision-making task and, in this way, learns to imitate the processes underlying subjects’ choices and their learning abilities. We demonstrate the benefits of this approach using a new dataset drawn from patients with either unipolar (n = 34) or bipolar (n = 33) depression and matched healthy controls (n = 34) making decisions on a two-armed bandit task. The results indicate that this new approach is better than baseline reinforcement-learning methods in terms of overall performance and its capacity to predict subjects’ choices. We show that the model can be interpreted using off-policy simulations and thereby provides a novel clustering of subjects’ learning processes—something that often eludes traditional approaches to modelling and behavioural analysis.

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 Dates: 2019-06
 Publication Status: Published online
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 Identifiers: DOI: 10.1371/journal.pcbi.1006903
eDoc: e1006903
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 16 (6) Sequence Number: - Start / End Page: 1 - 33 Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1