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Optimal policy for attention-modulated decisions explains human fixation behavior

Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations...

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Autores principales: Jang, Anthony I, Sharma, Ravi, Drugowitsch, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8064754/
https://www.ncbi.nlm.nih.gov/pubmed/33769284
http://dx.doi.org/10.7554/eLife.63436
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author Jang, Anthony I
Sharma, Ravi
Drugowitsch, Jan
author_facet Jang, Anthony I
Sharma, Ravi
Drugowitsch, Jan
author_sort Jang, Anthony I
collection PubMed
description Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations also causally affect one’s choices, biasing them toward the longer-fixated item (Krajbich et al., 2010). We derive a normative decision-making model in which attention enhances the reliability of information, consistent with neurophysiological findings (Cohen and Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation-related choice biases seen in humans and provides a Bayesian computational rationale for this phenomenon. This insight led to additional predictions that we could confirm in human data. Finally, by varying the relative cognitive advantage conferred by attention, we show that decision performance is benefited by a balanced spread of resources between the attended and unattended items.
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spelling pubmed-80647542021-04-29 Optimal policy for attention-modulated decisions explains human fixation behavior Jang, Anthony I Sharma, Ravi Drugowitsch, Jan eLife Neuroscience Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations also causally affect one’s choices, biasing them toward the longer-fixated item (Krajbich et al., 2010). We derive a normative decision-making model in which attention enhances the reliability of information, consistent with neurophysiological findings (Cohen and Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation-related choice biases seen in humans and provides a Bayesian computational rationale for this phenomenon. This insight led to additional predictions that we could confirm in human data. Finally, by varying the relative cognitive advantage conferred by attention, we show that decision performance is benefited by a balanced spread of resources between the attended and unattended items. eLife Sciences Publications, Ltd 2021-03-26 /pmc/articles/PMC8064754/ /pubmed/33769284 http://dx.doi.org/10.7554/eLife.63436 Text en © 2021, Jang et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Jang, Anthony I
Sharma, Ravi
Drugowitsch, Jan
Optimal policy for attention-modulated decisions explains human fixation behavior
title Optimal policy for attention-modulated decisions explains human fixation behavior
title_full Optimal policy for attention-modulated decisions explains human fixation behavior
title_fullStr Optimal policy for attention-modulated decisions explains human fixation behavior
title_full_unstemmed Optimal policy for attention-modulated decisions explains human fixation behavior
title_short Optimal policy for attention-modulated decisions explains human fixation behavior
title_sort optimal policy for attention-modulated decisions explains human fixation behavior
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8064754/
https://www.ncbi.nlm.nih.gov/pubmed/33769284
http://dx.doi.org/10.7554/eLife.63436
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