Cargando…

Human online adaptation to changes in prior probability

Optimal sensory decision-making requires the combination of uncertain sensory signals with prior expectations. The effect of prior probability is often described as a shift in the decision criterion. Can observers track sudden changes in probability? To answer this question, we used a change-point d...

Descripción completa

Detalles Bibliográficos
Autores principales: Norton, Elyse H., Acerbi, Luigi, Ma, Wei Ji, Landy, Michael S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6638982/
https://www.ncbi.nlm.nih.gov/pubmed/31283765
http://dx.doi.org/10.1371/journal.pcbi.1006681
_version_ 1783436389513166848
author Norton, Elyse H.
Acerbi, Luigi
Ma, Wei Ji
Landy, Michael S.
author_facet Norton, Elyse H.
Acerbi, Luigi
Ma, Wei Ji
Landy, Michael S.
author_sort Norton, Elyse H.
collection PubMed
description Optimal sensory decision-making requires the combination of uncertain sensory signals with prior expectations. The effect of prior probability is often described as a shift in the decision criterion. Can observers track sudden changes in probability? To answer this question, we used a change-point detection paradigm that is frequently used to examine behavior in changing environments. In a pair of orientation-categorization tasks, we investigated the effects of changing probabilities on decision-making. In both tasks, category probability was updated using a sample-and-hold procedure: probability was held constant for a period of time before jumping to another probability state that was randomly selected from a predetermined set of probability states. We developed an ideal Bayesian change-point detection model in which the observer marginalizes over both the current run length (i.e., time since last change) and the current category probability. We compared this model to various alternative models that correspond to different strategies—from approximately Bayesian to simple heuristics—that the observers may have adopted to update their beliefs about probabilities. While a number of models provided decent fits to the data, model comparison favored a model in which probability is estimated following an exponential averaging model with a bias towards equal priors, consistent with a conservative bias, and a flexible variant of the Bayesian change-point detection model with incorrect beliefs. We interpret the former as a simpler, more biologically plausible explanation suggesting that the mechanism underlying change of decision criterion is a combination of on-line estimation of prior probability and a stable, long-term equal-probability prior, thus operating at two very different timescales.
format Online
Article
Text
id pubmed-6638982
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-66389822019-07-25 Human online adaptation to changes in prior probability Norton, Elyse H. Acerbi, Luigi Ma, Wei Ji Landy, Michael S. PLoS Comput Biol Research Article Optimal sensory decision-making requires the combination of uncertain sensory signals with prior expectations. The effect of prior probability is often described as a shift in the decision criterion. Can observers track sudden changes in probability? To answer this question, we used a change-point detection paradigm that is frequently used to examine behavior in changing environments. In a pair of orientation-categorization tasks, we investigated the effects of changing probabilities on decision-making. In both tasks, category probability was updated using a sample-and-hold procedure: probability was held constant for a period of time before jumping to another probability state that was randomly selected from a predetermined set of probability states. We developed an ideal Bayesian change-point detection model in which the observer marginalizes over both the current run length (i.e., time since last change) and the current category probability. We compared this model to various alternative models that correspond to different strategies—from approximately Bayesian to simple heuristics—that the observers may have adopted to update their beliefs about probabilities. While a number of models provided decent fits to the data, model comparison favored a model in which probability is estimated following an exponential averaging model with a bias towards equal priors, consistent with a conservative bias, and a flexible variant of the Bayesian change-point detection model with incorrect beliefs. We interpret the former as a simpler, more biologically plausible explanation suggesting that the mechanism underlying change of decision criterion is a combination of on-line estimation of prior probability and a stable, long-term equal-probability prior, thus operating at two very different timescales. Public Library of Science 2019-07-08 /pmc/articles/PMC6638982/ /pubmed/31283765 http://dx.doi.org/10.1371/journal.pcbi.1006681 Text en © 2019 Norton 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
Norton, Elyse H.
Acerbi, Luigi
Ma, Wei Ji
Landy, Michael S.
Human online adaptation to changes in prior probability
title Human online adaptation to changes in prior probability
title_full Human online adaptation to changes in prior probability
title_fullStr Human online adaptation to changes in prior probability
title_full_unstemmed Human online adaptation to changes in prior probability
title_short Human online adaptation to changes in prior probability
title_sort human online adaptation to changes in prior probability
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6638982/
https://www.ncbi.nlm.nih.gov/pubmed/31283765
http://dx.doi.org/10.1371/journal.pcbi.1006681
work_keys_str_mv AT nortonelyseh humanonlineadaptationtochangesinpriorprobability
AT acerbiluigi humanonlineadaptationtochangesinpriorprobability
AT maweiji humanonlineadaptationtochangesinpriorprobability
AT landymichaels humanonlineadaptationtochangesinpriorprobability