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  Retrospective model-based inference guides model-free credit assignment

Moran, R., Keramati, M., Dayan, P., & Dolan, R. (2019). Retrospective model-based inference guides model-free credit assignment. Nature Communications, 10: 750, pp. 1-14. doi:10.1038/s41467-019-08662-8.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-0353-C Version Permalink: http://hdl.handle.net/21.11116/0000-0003-0354-B
Genre: Journal Article

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 Creators:
Moran, R, Author
Keramati, M, Author
Dayan, P1, 2, Author              
Dolan, RJ, 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: An extensive reinforcement learning literature shows that organisms assign credit efficiently, even under conditions of state uncertainty. However, little is known about credit-assignment when state uncertainty is subsequently resolved. Here, we address this problem within the framework of an interaction between model-free (MF) and model-based (MB) control systems. We present and support experimentally a theory of MB retrospective-inference. Within this framework, a MB system resolves uncertainty that prevailed when actions were taken thus guiding an MF credit-assignment. Using a task in which there was initial uncertainty about the lotteries that were chosen, we found that when participants' momentary uncertainty about which lottery had generated an outcome was resolved by provision of subsequent information, participants preferentially assigned credit within a MF system to the lottery they retrospectively inferred was responsible for this outcome. These findings extend our knowledge about the range of MB functions and the scope of system interactions.

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 Dates: 2019-02
 Publication Status: Published online
 Pages: -
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 Rev. Method: -
 Identifiers: DOI: 10.1038/s41467-019-08662-8
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Title: Nature Communications
  Abbreviation : Nat. Commun.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: London : Nature Publishing Group
Pages: - Volume / Issue: 10 Sequence Number: 750 Start / End Page: 1 - 14 Identifier: ISSN: 2041-1723
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1723