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  Learning to use past evidence in a sophisticated world model

Ahilan, S., Solomon, R., Breton, A.-Y., Conover, K., Niyogi, R., Shizgal, P., et al. (2019). Learning to use past evidence in a sophisticated world model. PLoS Computational Biology, 15(6), 1-20. doi:10.1371/journal.pcbi.1007093.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-DDC3-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-FE47-0
Genre: Journal Article

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 Creators:
Ahilan, S, Author
Solomon, RB, Author
Breton, A-Y, Author
Conover, K, Author
Niyogi, RK, Author
Shizgal, P, Author
Dayan, P1, 2, 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: Humans and other animals are able to discover underlying statistical structure in their environments and exploit it to achieve efficient and effective performance. However, such structure is often difficult to learn and use because it is obscure, involving long-range temporal dependencies. Here, we analysed behavioural data from an extended experiment with rats, showing that the subjects learned the underlying statistical structure, albeit suffering at times from immediate inferential imperfections as to their current state within it. We accounted for their behaviour using a Hidden Markov Model, in which recent observations are integrated with evidence from the past. We found that over the course of training, subjects came to track their progress through the task more accurately, a change that our model largely attributed to improved integration of past evidence. This learning reflected the structure of the task, decreasing reliance on recent observations, which were potentially misleading.

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 Dates: 2019-06
 Publication Status: Published online
 Pages: -
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 Rev. Method: -
 Identifiers: DOI: 10.1371/journal.pcbi.1007093
eDoc: e1007093
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Title: PLoS Computational Biology
Source Genre: Journal
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Affiliations:
Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 15 (6) Sequence Number: - Start / End Page: 1 - 20 Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1