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Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales
Behavior deviating from our normative expectations often appears irrational. For example, even though behavior following the so-called matching law can maximize reward in a stationary foraging task, actual behavior commonly deviates from matching. Such behavioral deviations are interpreted as a fail...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6443814/ https://www.ncbi.nlm.nih.gov/pubmed/30931937 http://dx.doi.org/10.1038/s41467-019-09388-3 |
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author | Iigaya, Kiyohito Ahmadian, Yashar Sugrue, Leo P. Corrado, Greg S. Loewenstein, Yonatan Newsome, William T. Fusi, Stefano |
author_facet | Iigaya, Kiyohito Ahmadian, Yashar Sugrue, Leo P. Corrado, Greg S. Loewenstein, Yonatan Newsome, William T. Fusi, Stefano |
author_sort | Iigaya, Kiyohito |
collection | PubMed |
description | Behavior deviating from our normative expectations often appears irrational. For example, even though behavior following the so-called matching law can maximize reward in a stationary foraging task, actual behavior commonly deviates from matching. Such behavioral deviations are interpreted as a failure of the subject; however, here we instead suggest that they reflect an adaptive strategy, suitable for uncertain, non-stationary environments. To prove it, we analyzed the behavior of primates that perform a dynamic foraging task. In such nonstationary environment, learning on both fast and slow timescales is beneficial: fast learning allows the animal to react to sudden changes, at the price of large fluctuations (variance) in the estimates of task relevant variables. Slow learning reduces the fluctuations but costs a bias that causes systematic behavioral deviations. Our behavioral analysis shows that the animals solved this bias-variance tradeoff by combining learning on both fast and slow timescales, suggesting that learning on multiple timescales can be a biologically plausible mechanism for optimizing decisions under uncertainty. |
format | Online Article Text |
id | pubmed-6443814 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-64438142019-04-03 Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales Iigaya, Kiyohito Ahmadian, Yashar Sugrue, Leo P. Corrado, Greg S. Loewenstein, Yonatan Newsome, William T. Fusi, Stefano Nat Commun Article Behavior deviating from our normative expectations often appears irrational. For example, even though behavior following the so-called matching law can maximize reward in a stationary foraging task, actual behavior commonly deviates from matching. Such behavioral deviations are interpreted as a failure of the subject; however, here we instead suggest that they reflect an adaptive strategy, suitable for uncertain, non-stationary environments. To prove it, we analyzed the behavior of primates that perform a dynamic foraging task. In such nonstationary environment, learning on both fast and slow timescales is beneficial: fast learning allows the animal to react to sudden changes, at the price of large fluctuations (variance) in the estimates of task relevant variables. Slow learning reduces the fluctuations but costs a bias that causes systematic behavioral deviations. Our behavioral analysis shows that the animals solved this bias-variance tradeoff by combining learning on both fast and slow timescales, suggesting that learning on multiple timescales can be a biologically plausible mechanism for optimizing decisions under uncertainty. Nature Publishing Group UK 2019-04-01 /pmc/articles/PMC6443814/ /pubmed/30931937 http://dx.doi.org/10.1038/s41467-019-09388-3 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Iigaya, Kiyohito Ahmadian, Yashar Sugrue, Leo P. Corrado, Greg S. Loewenstein, Yonatan Newsome, William T. Fusi, Stefano Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales |
title | Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales |
title_full | Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales |
title_fullStr | Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales |
title_full_unstemmed | Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales |
title_short | Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales |
title_sort | deviation from the matching law reflects an optimal strategy involving learning over multiple timescales |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6443814/ https://www.ncbi.nlm.nih.gov/pubmed/30931937 http://dx.doi.org/10.1038/s41467-019-09388-3 |
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