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Testing Multi-Alternative Decision Models with Non-Stationary Evidence

Recent research has investigated the process of integrating perceptual evidence toward a decision, converging on a number of sequential sampling choice models, such as variants of race and diffusion models and the non-linear leaky competing accumulator (LCA) model. Here we study extensions of these...

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Detalles Bibliográficos
Autores principales: Tsetsos, Konstantinos, Usher, Marius, McClelland, James L.
Formato: Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3093747/
https://www.ncbi.nlm.nih.gov/pubmed/21603227
http://dx.doi.org/10.3389/fnins.2011.00063
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author Tsetsos, Konstantinos
Usher, Marius
McClelland, James L.
author_facet Tsetsos, Konstantinos
Usher, Marius
McClelland, James L.
author_sort Tsetsos, Konstantinos
collection PubMed
description Recent research has investigated the process of integrating perceptual evidence toward a decision, converging on a number of sequential sampling choice models, such as variants of race and diffusion models and the non-linear leaky competing accumulator (LCA) model. Here we study extensions of these models to multi-alternative choice, considering how well they can account for data from a psychophysical experiment in which the evidence supporting each of the alternatives changes dynamically during the trial, in a way that creates temporal correlations. We find that participants exhibit a tendency to choose an alternative whose evidence profile is temporally anti-correlated with (or dissimilar from) that of other alternatives. This advantage of the anti-correlated alternative is well accounted for in the LCA, and provides constraints that challenge several other models of multi-alternative choice.
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spelling pubmed-30937472011-05-20 Testing Multi-Alternative Decision Models with Non-Stationary Evidence Tsetsos, Konstantinos Usher, Marius McClelland, James L. Front Neurosci Neuroscience Recent research has investigated the process of integrating perceptual evidence toward a decision, converging on a number of sequential sampling choice models, such as variants of race and diffusion models and the non-linear leaky competing accumulator (LCA) model. Here we study extensions of these models to multi-alternative choice, considering how well they can account for data from a psychophysical experiment in which the evidence supporting each of the alternatives changes dynamically during the trial, in a way that creates temporal correlations. We find that participants exhibit a tendency to choose an alternative whose evidence profile is temporally anti-correlated with (or dissimilar from) that of other alternatives. This advantage of the anti-correlated alternative is well accounted for in the LCA, and provides constraints that challenge several other models of multi-alternative choice. Frontiers Research Foundation 2011-05-04 /pmc/articles/PMC3093747/ /pubmed/21603227 http://dx.doi.org/10.3389/fnins.2011.00063 Text en Copyright © 2011 Tsetsos, Usher and McClelland. http://www.frontiersin.org/licenseagreement This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.
spellingShingle Neuroscience
Tsetsos, Konstantinos
Usher, Marius
McClelland, James L.
Testing Multi-Alternative Decision Models with Non-Stationary Evidence
title Testing Multi-Alternative Decision Models with Non-Stationary Evidence
title_full Testing Multi-Alternative Decision Models with Non-Stationary Evidence
title_fullStr Testing Multi-Alternative Decision Models with Non-Stationary Evidence
title_full_unstemmed Testing Multi-Alternative Decision Models with Non-Stationary Evidence
title_short Testing Multi-Alternative Decision Models with Non-Stationary Evidence
title_sort testing multi-alternative decision models with non-stationary evidence
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3093747/
https://www.ncbi.nlm.nih.gov/pubmed/21603227
http://dx.doi.org/10.3389/fnins.2011.00063
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