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Serial reproduction reveals the geometry of visuospatial representations

An essential function of the human visual system is to locate objects in space and navigate the environment. Due to limited resources, the visual system achieves this by combining imperfect sensory information with a belief state about locations in a scene, resulting in systematic distortions and bi...

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Autores principales: Langlois, Thomas A., Jacoby, Nori, Suchow, Jordan W., Griffiths, Thomas L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8020772/
https://www.ncbi.nlm.nih.gov/pubmed/33771919
http://dx.doi.org/10.1073/pnas.2012938118
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author Langlois, Thomas A.
Jacoby, Nori
Suchow, Jordan W.
Griffiths, Thomas L.
author_facet Langlois, Thomas A.
Jacoby, Nori
Suchow, Jordan W.
Griffiths, Thomas L.
author_sort Langlois, Thomas A.
collection PubMed
description An essential function of the human visual system is to locate objects in space and navigate the environment. Due to limited resources, the visual system achieves this by combining imperfect sensory information with a belief state about locations in a scene, resulting in systematic distortions and biases. These biases can be captured by a Bayesian model in which internal beliefs are expressed in a prior probability distribution over locations in a scene. We introduce a paradigm that enables us to measure these priors by iterating a simple memory task where the response of one participant becomes the stimulus for the next. This approach reveals an unprecedented richness and level of detail in these priors, suggesting a different way to think about biases in spatial memory. A prior distribution on locations in a visual scene can reflect the selective allocation of coding resources to different visual regions during encoding (“efficient encoding”). This selective allocation predicts that locations in the scene will be encoded with variable precision, in contrast to previous work that has assumed fixed encoding precision regardless of location. We demonstrate that perceptual biases covary with variations in discrimination accuracy, a finding that is aligned with simulations of our efficient encoding model but not the traditional fixed encoding view. This work demonstrates the promise of using nonparametric data-driven approaches that combine crowdsourcing with the careful curation of information transmission within social networks to reveal the hidden structure of shared visual representations.
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spelling pubmed-80207722021-04-13 Serial reproduction reveals the geometry of visuospatial representations Langlois, Thomas A. Jacoby, Nori Suchow, Jordan W. Griffiths, Thomas L. Proc Natl Acad Sci U S A Social Sciences An essential function of the human visual system is to locate objects in space and navigate the environment. Due to limited resources, the visual system achieves this by combining imperfect sensory information with a belief state about locations in a scene, resulting in systematic distortions and biases. These biases can be captured by a Bayesian model in which internal beliefs are expressed in a prior probability distribution over locations in a scene. We introduce a paradigm that enables us to measure these priors by iterating a simple memory task where the response of one participant becomes the stimulus for the next. This approach reveals an unprecedented richness and level of detail in these priors, suggesting a different way to think about biases in spatial memory. A prior distribution on locations in a visual scene can reflect the selective allocation of coding resources to different visual regions during encoding (“efficient encoding”). This selective allocation predicts that locations in the scene will be encoded with variable precision, in contrast to previous work that has assumed fixed encoding precision regardless of location. We demonstrate that perceptual biases covary with variations in discrimination accuracy, a finding that is aligned with simulations of our efficient encoding model but not the traditional fixed encoding view. This work demonstrates the promise of using nonparametric data-driven approaches that combine crowdsourcing with the careful curation of information transmission within social networks to reveal the hidden structure of shared visual representations. National Academy of Sciences 2021-03-30 2021-03-26 /pmc/articles/PMC8020772/ /pubmed/33771919 http://dx.doi.org/10.1073/pnas.2012938118 Text en Copyright © 2021 the Author(s). Published by PNAS. http://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (http://creativecommons.org/licenses/by/4.0/) .
spellingShingle Social Sciences
Langlois, Thomas A.
Jacoby, Nori
Suchow, Jordan W.
Griffiths, Thomas L.
Serial reproduction reveals the geometry of visuospatial representations
title Serial reproduction reveals the geometry of visuospatial representations
title_full Serial reproduction reveals the geometry of visuospatial representations
title_fullStr Serial reproduction reveals the geometry of visuospatial representations
title_full_unstemmed Serial reproduction reveals the geometry of visuospatial representations
title_short Serial reproduction reveals the geometry of visuospatial representations
title_sort serial reproduction reveals the geometry of visuospatial representations
topic Social Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8020772/
https://www.ncbi.nlm.nih.gov/pubmed/33771919
http://dx.doi.org/10.1073/pnas.2012938118
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