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Bayesian regression explains how human participants handle parameter uncertainty
Accumulating evidence indicates that the human brain copes with sensory uncertainty in accordance with Bayes’ rule. However, it is unknown how humans make predictions when the generative model of the task at hand is described by uncertain parameters. Here, we tested whether and how humans take param...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7259793/ https://www.ncbi.nlm.nih.gov/pubmed/32421708 http://dx.doi.org/10.1371/journal.pcbi.1007886 |
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author | Jegminat, Jannes Jastrzębowska, Maya A. Pachai, Matthew V. Herzog, Michael H. Pfister, Jean-Pascal |
author_facet | Jegminat, Jannes Jastrzębowska, Maya A. Pachai, Matthew V. Herzog, Michael H. Pfister, Jean-Pascal |
author_sort | Jegminat, Jannes |
collection | PubMed |
description | Accumulating evidence indicates that the human brain copes with sensory uncertainty in accordance with Bayes’ rule. However, it is unknown how humans make predictions when the generative model of the task at hand is described by uncertain parameters. Here, we tested whether and how humans take parameter uncertainty into account in a regression task. Participants extrapolated a parabola from a limited number of noisy points, shown on a computer screen. The quadratic parameter was drawn from a bimodal prior distribution. We tested whether human observers take full advantage of the given information, including the likelihood of the quadratic parameter value given the observed points and the quadratic parameter’s prior distribution. We compared human performance with Bayesian regression, which is the (Bayes) optimal solution to this problem, and three sub-optimal models, which are simpler to compute. Our results show that, under our specific experimental conditions, humans behave in a way that is consistent with Bayesian regression. Moreover, our results support the hypothesis that humans generate responses in a manner consistent with probability matching rather than Bayesian decision theory. |
format | Online Article Text |
id | pubmed-7259793 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-72597932020-06-08 Bayesian regression explains how human participants handle parameter uncertainty Jegminat, Jannes Jastrzębowska, Maya A. Pachai, Matthew V. Herzog, Michael H. Pfister, Jean-Pascal PLoS Comput Biol Research Article Accumulating evidence indicates that the human brain copes with sensory uncertainty in accordance with Bayes’ rule. However, it is unknown how humans make predictions when the generative model of the task at hand is described by uncertain parameters. Here, we tested whether and how humans take parameter uncertainty into account in a regression task. Participants extrapolated a parabola from a limited number of noisy points, shown on a computer screen. The quadratic parameter was drawn from a bimodal prior distribution. We tested whether human observers take full advantage of the given information, including the likelihood of the quadratic parameter value given the observed points and the quadratic parameter’s prior distribution. We compared human performance with Bayesian regression, which is the (Bayes) optimal solution to this problem, and three sub-optimal models, which are simpler to compute. Our results show that, under our specific experimental conditions, humans behave in a way that is consistent with Bayesian regression. Moreover, our results support the hypothesis that humans generate responses in a manner consistent with probability matching rather than Bayesian decision theory. Public Library of Science 2020-05-18 /pmc/articles/PMC7259793/ /pubmed/32421708 http://dx.doi.org/10.1371/journal.pcbi.1007886 Text en © 2020 Jegminat 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 Jegminat, Jannes Jastrzębowska, Maya A. Pachai, Matthew V. Herzog, Michael H. Pfister, Jean-Pascal Bayesian regression explains how human participants handle parameter uncertainty |
title | Bayesian regression explains how human participants handle parameter uncertainty |
title_full | Bayesian regression explains how human participants handle parameter uncertainty |
title_fullStr | Bayesian regression explains how human participants handle parameter uncertainty |
title_full_unstemmed | Bayesian regression explains how human participants handle parameter uncertainty |
title_short | Bayesian regression explains how human participants handle parameter uncertainty |
title_sort | bayesian regression explains how human participants handle parameter uncertainty |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7259793/ https://www.ncbi.nlm.nih.gov/pubmed/32421708 http://dx.doi.org/10.1371/journal.pcbi.1007886 |
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