Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Jegminat, Jannes, Jastrzębowska, Maya A., Pachai, Matthew V., Herzog, Michael H., Pfister, Jean-Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
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
_version_ 1783540207067332608
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
work_keys_str_mv AT jegminatjannes bayesianregressionexplainshowhumanparticipantshandleparameteruncertainty
AT jastrzebowskamayaa bayesianregressionexplainshowhumanparticipantshandleparameteruncertainty
AT pachaimatthewv bayesianregressionexplainshowhumanparticipantshandleparameteruncertainty
AT herzogmichaelh bayesianregressionexplainshowhumanparticipantshandleparameteruncertainty
AT pfisterjeanpascal bayesianregressionexplainshowhumanparticipantshandleparameteruncertainty