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Value-based decision making via sequential sampling with hierarchical competition and attentional modulation

In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grou...

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Autor principal: Colas, Jaron T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5659783/
https://www.ncbi.nlm.nih.gov/pubmed/29077746
http://dx.doi.org/10.1371/journal.pone.0186822
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author Colas, Jaron T.
author_facet Colas, Jaron T.
author_sort Colas, Jaron T.
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description In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes “winner-take-all” processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans’ value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light.
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spelling pubmed-56597832017-11-09 Value-based decision making via sequential sampling with hierarchical competition and attentional modulation Colas, Jaron T. PLoS One Research Article In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes “winner-take-all” processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans’ value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light. Public Library of Science 2017-10-27 /pmc/articles/PMC5659783/ /pubmed/29077746 http://dx.doi.org/10.1371/journal.pone.0186822 Text en © 2017 Jaron T. Colas 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
Colas, Jaron T.
Value-based decision making via sequential sampling with hierarchical competition and attentional modulation
title Value-based decision making via sequential sampling with hierarchical competition and attentional modulation
title_full Value-based decision making via sequential sampling with hierarchical competition and attentional modulation
title_fullStr Value-based decision making via sequential sampling with hierarchical competition and attentional modulation
title_full_unstemmed Value-based decision making via sequential sampling with hierarchical competition and attentional modulation
title_short Value-based decision making via sequential sampling with hierarchical competition and attentional modulation
title_sort value-based decision making via sequential sampling with hierarchical competition and attentional modulation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5659783/
https://www.ncbi.nlm.nih.gov/pubmed/29077746
http://dx.doi.org/10.1371/journal.pone.0186822
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