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However, it raises the fundamental question of which individuals have reliable information: successful individuals versus the majority. The former and the latter are known respectively as success-based and conformist social learning strategies. We show here that while the success-based strategy fully exploits the benign environment of low uncertainly, it fails in uncertain environments. On the other hand, the conformist strategy can effectively mitigate this adverse effect. Based on these findings, we hypothesized that meta-control of individual and social learning strategies provides effective and sample-efficient learning in volatile and uncertain environments. Simulations on a set of environments with various levels of volatility and uncertainty confirmed our hypothesis. The results imply that meta-control of social learning affords agents the leverage to resolve environmental uncertainty with minimal exploration cost, by exploiting others\u2019 learning as an external knowledge base.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009882","type":"journal-article","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T18:33:56Z","timestamp":1646073236000},"page":"e1009882","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Meta-control of social learning strategies"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1379-3778","authenticated-orcid":true,"given":"Anil","family":"Yaman","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8241-7461","authenticated-orcid":true,"given":"Nicolas","family":"Bredeche","sequence":"additional","affiliation":[]},{"given":"Onur","family":"\u00c7aylak","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3153-916X","authenticated-orcid":true,"given":"Joel Z.","family":"Leibo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-9613","authenticated-orcid":true,"given":"Sang Wan","family":"Lee","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"issue":"3","key":"pcbi.1009882.ref001","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.neuron.2013.11.028","article-title":"Neural computations underlying arbitration between model-based and model-free learning","volume":"81","author":"SW Lee","year":"2014","journal-title":"Neuron"},{"issue":"5669","key":"pcbi.1009882.ref002","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1126\/science.1094285","article-title":"Dissociable Roles of Ventral and Dorsal Striatum in Instrumental Conditioning","volume":"304","author":"J O\u2019Doherty","year":"2004","journal-title":"Science"},{"issue":"12","key":"pcbi.1009882.ref003","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1038\/nn1560","article-title":"Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control","volume":"8","author":"ND Daw","year":"2005","journal-title":"Nature neuroscience"},{"issue":"6","key":"pcbi.1009882.ref004","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1038\/s41593-018-0147-8","article-title":"Prefrontal cortex as a meta-reinforcement learning system","volume":"21","author":"JX Wang","year":"2018","journal-title":"Nature neuroscience"},{"issue":"4","key":"pcbi.1009882.ref005","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1038\/s41583-020-0276-4","article-title":"The neural and computational systems of social learning","volume":"21","author":"A Olsson","year":"2020","journal-title":"Nature Reviews Neuroscience"},{"key":"pcbi.1009882.ref006","first-page":"1","article-title":"Meta-control: From psychology to computational neuroscience","author":"B Eppinger","year":"2021","journal-title":"Cognitive, Affective, & Behavioral Neuroscience"},{"issue":"4","key":"pcbi.1009882.ref007","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.neuron.2010.04.016","article-title":"States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning","volume":"66","author":"J Gl\u00e4scher","year":"2010","journal-title":"Neuron"},{"key":"pcbi.1009882.ref008","unstructured":"Lengyel M, Dayan P. 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