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Learning efficient representations of environmental priors in working memory
Experience shapes our expectations and helps us learn the structure of the environment. Inference models render such learning as a gradual refinement of the observer’s estimate of the environmental prior. For instance, when retaining an estimate of an object’s features in working memory, learned pri...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662764/ https://www.ncbi.nlm.nih.gov/pubmed/37943956 http://dx.doi.org/10.1371/journal.pcbi.1011622 |
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author | Eissa, Tahra L. Kilpatrick, Zachary P. |
author_facet | Eissa, Tahra L. Kilpatrick, Zachary P. |
author_sort | Eissa, Tahra L. |
collection | PubMed |
description | Experience shapes our expectations and helps us learn the structure of the environment. Inference models render such learning as a gradual refinement of the observer’s estimate of the environmental prior. For instance, when retaining an estimate of an object’s features in working memory, learned priors may bias the estimate in the direction of common feature values. Humans display such biases when retaining color estimates on short time intervals. We propose that these systematic biases emerge from modulation of synaptic connectivity in a neural circuit based on the experienced stimulus history, shaping the persistent and collective neural activity that encodes the stimulus estimate. Resulting neural activity attractors are aligned to common stimulus values. Using recently published human response data from a delayed-estimation task in which stimuli (colors) were drawn from a heterogeneous distribution that did not necessarily correspond with reported population biases, we confirm that most subjects’ response distributions are better described by experience-dependent learning models than by models with fixed biases. This work suggests systematic limitations in working memory reflect efficient representations of inferred environmental structure, providing new insights into how humans integrate environmental knowledge into their cognitive strategies. |
format | Online Article Text |
id | pubmed-10662764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-106627642023-11-09 Learning efficient representations of environmental priors in working memory Eissa, Tahra L. Kilpatrick, Zachary P. PLoS Comput Biol Research Article Experience shapes our expectations and helps us learn the structure of the environment. Inference models render such learning as a gradual refinement of the observer’s estimate of the environmental prior. For instance, when retaining an estimate of an object’s features in working memory, learned priors may bias the estimate in the direction of common feature values. Humans display such biases when retaining color estimates on short time intervals. We propose that these systematic biases emerge from modulation of synaptic connectivity in a neural circuit based on the experienced stimulus history, shaping the persistent and collective neural activity that encodes the stimulus estimate. Resulting neural activity attractors are aligned to common stimulus values. Using recently published human response data from a delayed-estimation task in which stimuli (colors) were drawn from a heterogeneous distribution that did not necessarily correspond with reported population biases, we confirm that most subjects’ response distributions are better described by experience-dependent learning models than by models with fixed biases. This work suggests systematic limitations in working memory reflect efficient representations of inferred environmental structure, providing new insights into how humans integrate environmental knowledge into their cognitive strategies. Public Library of Science 2023-11-09 /pmc/articles/PMC10662764/ /pubmed/37943956 http://dx.doi.org/10.1371/journal.pcbi.1011622 Text en © 2023 Eissa, Kilpatrick https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 Eissa, Tahra L. Kilpatrick, Zachary P. Learning efficient representations of environmental priors in working memory |
title | Learning efficient representations of environmental priors in working memory |
title_full | Learning efficient representations of environmental priors in working memory |
title_fullStr | Learning efficient representations of environmental priors in working memory |
title_full_unstemmed | Learning efficient representations of environmental priors in working memory |
title_short | Learning efficient representations of environmental priors in working memory |
title_sort | learning efficient representations of environmental priors in working memory |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662764/ https://www.ncbi.nlm.nih.gov/pubmed/37943956 http://dx.doi.org/10.1371/journal.pcbi.1011622 |
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