Cargando…
A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making
Real-life decisions often require a comparison of multi-attribute options with various benefits and costs, and the evaluation of each option depends partly on the others in the choice set (i.e., the choice context). Although reinforcement learning models have successfully described choice behavior,...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635580/ https://www.ncbi.nlm.nih.gov/pubmed/31354461 http://dx.doi.org/10.3389/fncom.2019.00040 |
_version_ | 1783435911020675072 |
---|---|
author | Jung, Kanghoon Jeong, Jaeseung Kralik, Jerald D. |
author_facet | Jung, Kanghoon Jeong, Jaeseung Kralik, Jerald D. |
author_sort | Jung, Kanghoon |
collection | PubMed |
description | Real-life decisions often require a comparison of multi-attribute options with various benefits and costs, and the evaluation of each option depends partly on the others in the choice set (i.e., the choice context). Although reinforcement learning models have successfully described choice behavior, how to account for multi-attribute information when making a context-dependent decision remains unclear. Here we develop a computational model of attention control that includes context effects on multi-attribute decisions, linking a context-dependent choice model with a reinforcement learning model. The overall model suggests that the distinctiveness of attributes guides an individual's preferences among multi-attribute options via an attention-control mechanism that determines whether choices are selectively biased toward the most distinctive attribute (selective attention) or proportionally distributed based on the relative distinctiveness of attributes (divided attention). To test the model, we conducted a behavioral experiment in rhesus monkeys, in which they made simple multi-attribute decisions over three conditions that manipulated the degree of distinctiveness between alternatives: (1) four foods of different size and calorie; (2) four pieces of the same food in different colors; and (3) four identical pieces of food. The model simulation of the choice behavior captured the preference bias (i.e., overall preference structure) and the choice persistence (repeated choices) in the empirical data, providing evidence for the respective influences of attention and memory on preference bias and choice persistence. Our study provides insights into computations underlying multi-attribute decisions, linking attentional control to decision-making processes. |
format | Online Article Text |
id | pubmed-6635580 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-66355802019-07-26 A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making Jung, Kanghoon Jeong, Jaeseung Kralik, Jerald D. Front Comput Neurosci Neuroscience Real-life decisions often require a comparison of multi-attribute options with various benefits and costs, and the evaluation of each option depends partly on the others in the choice set (i.e., the choice context). Although reinforcement learning models have successfully described choice behavior, how to account for multi-attribute information when making a context-dependent decision remains unclear. Here we develop a computational model of attention control that includes context effects on multi-attribute decisions, linking a context-dependent choice model with a reinforcement learning model. The overall model suggests that the distinctiveness of attributes guides an individual's preferences among multi-attribute options via an attention-control mechanism that determines whether choices are selectively biased toward the most distinctive attribute (selective attention) or proportionally distributed based on the relative distinctiveness of attributes (divided attention). To test the model, we conducted a behavioral experiment in rhesus monkeys, in which they made simple multi-attribute decisions over three conditions that manipulated the degree of distinctiveness between alternatives: (1) four foods of different size and calorie; (2) four pieces of the same food in different colors; and (3) four identical pieces of food. The model simulation of the choice behavior captured the preference bias (i.e., overall preference structure) and the choice persistence (repeated choices) in the empirical data, providing evidence for the respective influences of attention and memory on preference bias and choice persistence. Our study provides insights into computations underlying multi-attribute decisions, linking attentional control to decision-making processes. Frontiers Media S.A. 2019-07-10 /pmc/articles/PMC6635580/ /pubmed/31354461 http://dx.doi.org/10.3389/fncom.2019.00040 Text en Copyright © 2019 Jung, Jeong and Kralik. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Jung, Kanghoon Jeong, Jaeseung Kralik, Jerald D. A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making |
title | A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making |
title_full | A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making |
title_fullStr | A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making |
title_full_unstemmed | A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making |
title_short | A Computational Model of Attention Control in Multi-Attribute, Context-Dependent Decision Making |
title_sort | computational model of attention control in multi-attribute, context-dependent decision making |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635580/ https://www.ncbi.nlm.nih.gov/pubmed/31354461 http://dx.doi.org/10.3389/fncom.2019.00040 |
work_keys_str_mv | AT jungkanghoon acomputationalmodelofattentioncontrolinmultiattributecontextdependentdecisionmaking AT jeongjaeseung acomputationalmodelofattentioncontrolinmultiattributecontextdependentdecisionmaking AT kralikjeraldd acomputationalmodelofattentioncontrolinmultiattributecontextdependentdecisionmaking AT jungkanghoon computationalmodelofattentioncontrolinmultiattributecontextdependentdecisionmaking AT jeongjaeseung computationalmodelofattentioncontrolinmultiattributecontextdependentdecisionmaking AT kralikjeraldd computationalmodelofattentioncontrolinmultiattributecontextdependentdecisionmaking |