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A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model

Social and decision-making deficits are often the first symptoms of neuropsychiatric disorders. In recent years, economic games, together with computational models of strategic learning, have been increasingly applied to the characterization of individual differences in social behavior, as well as t...

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Autores principales: Zhang, Zhihao, Chandra, Saksham, Kayser, Andrew, Hsu, Ming, Warren, Joshua L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MIT Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790055/
https://www.ncbi.nlm.nih.gov/pubmed/33426270
http://dx.doi.org/10.1162/cpsy_a_00028
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author Zhang, Zhihao
Chandra, Saksham
Kayser, Andrew
Hsu, Ming
Warren, Joshua L.
author_facet Zhang, Zhihao
Chandra, Saksham
Kayser, Andrew
Hsu, Ming
Warren, Joshua L.
author_sort Zhang, Zhihao
collection PubMed
description Social and decision-making deficits are often the first symptoms of neuropsychiatric disorders. In recent years, economic games, together with computational models of strategic learning, have been increasingly applied to the characterization of individual differences in social behavior, as well as their changes across time due to disease progression, treatment, or other factors. At the same time, the high dimensionality of these data poses an important challenge to statistical estimation of these models, potentially limiting the adoption of such approaches in patients and special populations. We introduce a hierarchical Bayesian implementation of a class of strategic learning models, experience-weighted attraction (EWA), that is widely used in behavioral game theory. Importantly, this approach provides a unified framework for capturing between- and within-participant variation, including changes associated with disease progression, comorbidity, and treatment status. We show using simulated data that our hierarchical Bayesian approach outperforms representative agent and individual-level estimation methods that are commonly used in extant literature, with respect to parameter estimation and uncertainty quantification. Furthermore, using an empirical dataset, we demonstrate the value of our approach over competing methods with respect to balancing model fit and complexity. Consistent with the success of hierarchical Bayesian approaches in other areas of behavioral science, our hierarchical Bayesian EWA model represents a powerful and flexible tool to apply to a wide range of behavioral paradigms for studying the interplay between complex human behavior and biological factors.
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spelling pubmed-77900552021-01-08 A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model Zhang, Zhihao Chandra, Saksham Kayser, Andrew Hsu, Ming Warren, Joshua L. Comput Psychiatr Research Articles Social and decision-making deficits are often the first symptoms of neuropsychiatric disorders. In recent years, economic games, together with computational models of strategic learning, have been increasingly applied to the characterization of individual differences in social behavior, as well as their changes across time due to disease progression, treatment, or other factors. At the same time, the high dimensionality of these data poses an important challenge to statistical estimation of these models, potentially limiting the adoption of such approaches in patients and special populations. We introduce a hierarchical Bayesian implementation of a class of strategic learning models, experience-weighted attraction (EWA), that is widely used in behavioral game theory. Importantly, this approach provides a unified framework for capturing between- and within-participant variation, including changes associated with disease progression, comorbidity, and treatment status. We show using simulated data that our hierarchical Bayesian approach outperforms representative agent and individual-level estimation methods that are commonly used in extant literature, with respect to parameter estimation and uncertainty quantification. Furthermore, using an empirical dataset, we demonstrate the value of our approach over competing methods with respect to balancing model fit and complexity. Consistent with the success of hierarchical Bayesian approaches in other areas of behavioral science, our hierarchical Bayesian EWA model represents a powerful and flexible tool to apply to a wide range of behavioral paradigms for studying the interplay between complex human behavior and biological factors. MIT Press 2020-11 /pmc/articles/PMC7790055/ /pubmed/33426270 http://dx.doi.org/10.1162/cpsy_a_00028 Text en © 2020 Computational Psychiatry, Inc. and the Massachusetts Institute of Technology This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.
spellingShingle Research Articles
Zhang, Zhihao
Chandra, Saksham
Kayser, Andrew
Hsu, Ming
Warren, Joshua L.
A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model
title A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model
title_full A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model
title_fullStr A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model
title_full_unstemmed A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model
title_short A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model
title_sort hierarchical bayesian implementation of the experience-weighted attraction model
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790055/
https://www.ncbi.nlm.nih.gov/pubmed/33426270
http://dx.doi.org/10.1162/cpsy_a_00028
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