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Learning to use past evidence in a sophisticated world model
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611652/ https://www.ncbi.nlm.nih.gov/pubmed/31233559 http://dx.doi.org/10.1371/journal.pcbi.1007093 |
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author | Ahilan, Sanjeevan Solomon, Rebecca B. Breton, Yannick-André Conover, Kent Niyogi, Ritwik K. Shizgal, Peter Dayan, Peter |
author_facet | Ahilan, Sanjeevan Solomon, Rebecca B. Breton, Yannick-André Conover, Kent Niyogi, Ritwik K. Shizgal, Peter Dayan, Peter |
author_sort | Ahilan, Sanjeevan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6611652 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-66116522019-07-12 Learning to use past evidence in a sophisticated world model Ahilan, Sanjeevan Solomon, Rebecca B. Breton, Yannick-André Conover, Kent Niyogi, Ritwik K. Shizgal, Peter Dayan, Peter PLoS Comput Biol Research Article 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. Public Library of Science 2019-06-24 /pmc/articles/PMC6611652/ /pubmed/31233559 http://dx.doi.org/10.1371/journal.pcbi.1007093 Text en © 2019 Ahilan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ahilan, Sanjeevan Solomon, Rebecca B. Breton, Yannick-André Conover, Kent Niyogi, Ritwik K. Shizgal, Peter Dayan, Peter Learning to use past evidence in a sophisticated world model |
title | Learning to use past evidence in a sophisticated world model |
title_full | Learning to use past evidence in a sophisticated world model |
title_fullStr | Learning to use past evidence in a sophisticated world model |
title_full_unstemmed | Learning to use past evidence in a sophisticated world model |
title_short | Learning to use past evidence in a sophisticated world model |
title_sort | learning to use past evidence in a sophisticated world model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611652/ https://www.ncbi.nlm.nih.gov/pubmed/31233559 http://dx.doi.org/10.1371/journal.pcbi.1007093 |
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