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Robust averaging protects decisions from noise in neural computations

An ideal observer will give equivalent weight to sources of information that are equally reliable. However, when averaging visual information, human observers tend to downweight or discount features that are relatively outlying or deviant (‘robust averaging’). Why humans adopt an integration policy...

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Detalles Bibliográficos
Autores principales: Li, Vickie, Herce Castañón, Santiago, Solomon, Joshua A., Vandormael, Hildward, Summerfield, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5589265/
https://www.ncbi.nlm.nih.gov/pubmed/28841644
http://dx.doi.org/10.1371/journal.pcbi.1005723
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author Li, Vickie
Herce Castañón, Santiago
Solomon, Joshua A.
Vandormael, Hildward
Summerfield, Christopher
author_facet Li, Vickie
Herce Castañón, Santiago
Solomon, Joshua A.
Vandormael, Hildward
Summerfield, Christopher
author_sort Li, Vickie
collection PubMed
description An ideal observer will give equivalent weight to sources of information that are equally reliable. However, when averaging visual information, human observers tend to downweight or discount features that are relatively outlying or deviant (‘robust averaging’). Why humans adopt an integration policy that discards important decision information remains unknown. Here, observers were asked to judge the average tilt in a circular array of high-contrast gratings, relative to an orientation boundary defined by a central reference grating. Observers showed robust averaging of orientation, but the extent to which they did so was a positive predictor of their overall performance. Using computational simulations, we show that although robust averaging is suboptimal for a perfect integrator, it paradoxically enhances performance in the presence of “late” noise, i.e. which corrupts decisions during integration. In other words, robust decision strategies increase the brain’s resilience to noise arising in neural computations during decision-making.
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spelling pubmed-55892652017-09-15 Robust averaging protects decisions from noise in neural computations Li, Vickie Herce Castañón, Santiago Solomon, Joshua A. Vandormael, Hildward Summerfield, Christopher PLoS Comput Biol Research Article An ideal observer will give equivalent weight to sources of information that are equally reliable. However, when averaging visual information, human observers tend to downweight or discount features that are relatively outlying or deviant (‘robust averaging’). Why humans adopt an integration policy that discards important decision information remains unknown. Here, observers were asked to judge the average tilt in a circular array of high-contrast gratings, relative to an orientation boundary defined by a central reference grating. Observers showed robust averaging of orientation, but the extent to which they did so was a positive predictor of their overall performance. Using computational simulations, we show that although robust averaging is suboptimal for a perfect integrator, it paradoxically enhances performance in the presence of “late” noise, i.e. which corrupts decisions during integration. In other words, robust decision strategies increase the brain’s resilience to noise arising in neural computations during decision-making. Public Library of Science 2017-08-25 /pmc/articles/PMC5589265/ /pubmed/28841644 http://dx.doi.org/10.1371/journal.pcbi.1005723 Text en © 2017 Li 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
Li, Vickie
Herce Castañón, Santiago
Solomon, Joshua A.
Vandormael, Hildward
Summerfield, Christopher
Robust averaging protects decisions from noise in neural computations
title Robust averaging protects decisions from noise in neural computations
title_full Robust averaging protects decisions from noise in neural computations
title_fullStr Robust averaging protects decisions from noise in neural computations
title_full_unstemmed Robust averaging protects decisions from noise in neural computations
title_short Robust averaging protects decisions from noise in neural computations
title_sort robust averaging protects decisions from noise in neural computations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5589265/
https://www.ncbi.nlm.nih.gov/pubmed/28841644
http://dx.doi.org/10.1371/journal.pcbi.1005723
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