{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:53:09Z","timestamp":1767178389252,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009549","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T00:00:00Z","timestamp":1637280000000}}],"reference-count":41,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T00:00:00Z","timestamp":1636416000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2016M3C7A1914448"],"award-info":[{"award-number":["2016M3C7A1914448"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2017M3C7A1031331"],"award-info":[{"award-number":["2017M3C7A1031331"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Controllability perception significantly influences motivated behavior and emotion and requires an estimation of one\u2019s influence on an environment. Previous studies have shown that an agent can infer controllability by observing contingency between one\u2019s 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\u2019s genuine controllability requires exclusion of other agents\u2019 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\u2019s action-outcome contingency information is integrated with one\u2019s 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.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009549","type":"journal-article","created":{"date-parts":[[2021,11,9]],"date-time":"2021-11-09T13:59:06Z","timestamp":1636466346000},"page":"e1009549","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Neurocomputational mechanism of controllability inference under a multi-agent setting"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2666-5153","authenticated-orcid":true,"given":"Jaejoong","family":"Kim","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-9613","authenticated-orcid":true,"given":"Sang Wan","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2200-3494","authenticated-orcid":true,"given":"Seokho","family":"Yoon","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2357-603X","authenticated-orcid":true,"given":"Haeorm","family":"Park","sequence":"additional","affiliation":[]},{"given":"Bumseok","family":"Jeong","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2021,11,9]]},"reference":[{"key":"pcbi.1009549.ref001","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3389\/fnins.2019.00065","article-title":"A reward-based framework of perceived control","volume":"13","author":"V Ly","year":"2019","journal-title":"Frontiers in neuroscience"},{"issue":"1","key":"pcbi.1009549.ref002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-14800-4","article-title":"Internal control beliefs shape positive affect and associated neural dynamics during outcome valuation","volume":"11","author":"DS Stolz","year":"2020","journal-title":"Nature Communications"},{"issue":"4","key":"pcbi.1009549.ref003","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1037\/rev0000033","article-title":"Learned helplessness at fifty: Insights from neuroscience.","volume":"123","author":"SF Maier","year":"2016","journal-title":"Psychological review"},{"issue":"10","key":"pcbi.1009549.ref004","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.tics.2017.06.008","article-title":"Agency and the calibration of motivated behavior.","volume":"21","author":"JM Moscarello","year":"2017","journal-title":"Trends in cognitive sciences"},{"issue":"3","key":"pcbi.1009549.ref005","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.cognition.2009.01.008","article-title":"A Bayesian formulation of behavioral control.","volume":"113","author":"QJ Huys","year":"2009","journal-title":"Cognition"},{"key":"pcbi.1009549.ref006","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.conb.2016.11.002","article-title":"Computational principles and models of multisensory integration","volume":"43","author":"C. 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