Neurocomputational mechanism of controllability inference under a multi-agent setting.

Controllability perception significantly influences motivated behavior and emotion and requires an estimation of one's influence on an environment. Previous studies have shown that an agent can infer controllability by observing contingency between one's own action and outcome if there are...

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Autores principales:	Jaejoong Kim, Sang Wan Lee, Seokho Yoon, Haeorm Park, Bumseok Jeong
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/019a57be072a4f23b2dfe3ba9be37085
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spelling	oai:doaj.org-article:019a57be072a4f23b2dfe3ba9be370852021-12-02T19:57:57ZNeurocomputational mechanism of controllability inference under a multi-agent setting.1553-734X1553-735810.1371/journal.pcbi.1009549https://doaj.org/article/019a57be072a4f23b2dfe3ba9be370852021-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009549https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Controllability perception significantly influences motivated behavior and emotion and requires an estimation of one's influence on an environment. Previous studies have shown that an agent can infer controllability by observing contingency between one's own action and outcome if there are no other outcome-relevant agents in an environment. However, if there are multiple agents who can influence the outcome, estimation of one's genuine controllability requires exclusion of other agents' possible influence. Here, we first investigated a computational and neural mechanism of controllability inference in a multi-agent setting. Our novel multi-agent Bayesian controllability inference model showed that other people's action-outcome contingency information is integrated with one's own action-outcome contingency to infer controllability, which can be explained as a Bayesian inference. Model-based functional MRI analyses showed that multi-agent Bayesian controllability inference recruits the temporoparietal junction (TPJ) and striatum. Then, this inferred controllability information was leveraged to increase motivated behavior in the vmPFC. These results generalize the previously known role of the striatum and vmPFC in single-agent controllability to multi-agent controllability, and this generalized role requires the TPJ in addition to the striatum of single-agent controllability to integrate both self- and other-related information. Finally, we identified an innate positive bias toward the self during the multi-agent controllability inference, which facilitated behavioral adaptation under volatile controllability. Furthermore, low positive bias and high negative bias were associated with increased daily feelings of guilt. Our results provide a mechanism of how our sense of controllability fluctuates due to other people in our lives, which might be related to social learned helplessness and depression.Jaejoong KimSang Wan LeeSeokho YoonHaeorm ParkBumseok JeongPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 11, p e1009549 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jaejoong Kim Sang Wan Lee Seokho Yoon Haeorm Park Bumseok Jeong Neurocomputational mechanism of controllability inference under a multi-agent setting.
description	Controllability perception significantly influences motivated behavior and emotion and requires an estimation of one's influence on an environment. Previous studies have shown that an agent can infer controllability by observing contingency between one's own action and outcome if there are no other outcome-relevant agents in an environment. However, if there are multiple agents who can influence the outcome, estimation of one's genuine controllability requires exclusion of other agents' possible influence. Here, we first investigated a computational and neural mechanism of controllability inference in a multi-agent setting. Our novel multi-agent Bayesian controllability inference model showed that other people's action-outcome contingency information is integrated with one's own action-outcome contingency to infer controllability, which can be explained as a Bayesian inference. Model-based functional MRI analyses showed that multi-agent Bayesian controllability inference recruits the temporoparietal junction (TPJ) and striatum. Then, this inferred controllability information was leveraged to increase motivated behavior in the vmPFC. These results generalize the previously known role of the striatum and vmPFC in single-agent controllability to multi-agent controllability, and this generalized role requires the TPJ in addition to the striatum of single-agent controllability to integrate both self- and other-related information. Finally, we identified an innate positive bias toward the self during the multi-agent controllability inference, which facilitated behavioral adaptation under volatile controllability. Furthermore, low positive bias and high negative bias were associated with increased daily feelings of guilt. Our results provide a mechanism of how our sense of controllability fluctuates due to other people in our lives, which might be related to social learned helplessness and depression.
format	article
author	Jaejoong Kim Sang Wan Lee Seokho Yoon Haeorm Park Bumseok Jeong
author_facet	Jaejoong Kim Sang Wan Lee Seokho Yoon Haeorm Park Bumseok Jeong
author_sort	Jaejoong Kim
title	Neurocomputational mechanism of controllability inference under a multi-agent setting.
title_short	Neurocomputational mechanism of controllability inference under a multi-agent setting.
title_full	Neurocomputational mechanism of controllability inference under a multi-agent setting.
title_fullStr	Neurocomputational mechanism of controllability inference under a multi-agent setting.
title_full_unstemmed	Neurocomputational mechanism of controllability inference under a multi-agent setting.
title_sort	neurocomputational mechanism of controllability inference under a multi-agent setting.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/019a57be072a4f23b2dfe3ba9be37085
work_keys_str_mv	AT jaejoongkim neurocomputationalmechanismofcontrollabilityinferenceunderamultiagentsetting AT sangwanlee neurocomputationalmechanismofcontrollabilityinferenceunderamultiagentsetting AT seokhoyoon neurocomputationalmechanismofcontrollabilityinferenceunderamultiagentsetting AT haeormpark neurocomputationalmechanismofcontrollabilityinferenceunderamultiagentsetting AT bumseokjeong neurocomputationalmechanismofcontrollabilityinferenceunderamultiagentsetting
_version_	1718375760560914432

Neurocomputational mechanism of controllability inference under a multi-agent setting.

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