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Neurocomputational approaches to social behavior


Konovalov, Arkady; Hu, Jie; Ruff, Christian C (2018). Neurocomputational approaches to social behavior. Current Opinion in Psychology, 24:41-47.

Abstract

Social decision-making is increasingly studied with neurocomputational modeling. Here we discuss how this approach allows researchers to better understand and predict behavior in social settings. Using examples from the study of resource distributions and social learning, we illustrate how this methodology provides a flexible way to quantify social values and beliefs, identify specific motives and cognitive processes underlying social choice and learning, and arbitrate between competing theories of social behavior. We also critically discuss open questions and potential problems associated with this methodology.

Abstract

Social decision-making is increasingly studied with neurocomputational modeling. Here we discuss how this approach allows researchers to better understand and predict behavior in social settings. Using examples from the study of resource distributions and social learning, we illustrate how this methodology provides a flexible way to quantify social values and beliefs, identify specific motives and cognitive processes underlying social choice and learning, and arbitrate between competing theories of social behavior. We also critically discuss open questions and potential problems associated with this methodology.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Date:December 2018
Deposited On:06 Jun 2018 10:14
Last Modified:01 Dec 2019 01:00
Publisher:Elsevier
ISSN:2352-250X
OA Status:Green
Publisher DOI:https://doi.org/10.1016/j.copsyc.2018.04.009
Official URL:https://www.sciencedirect.com/science/article/pii/S2352250X17302798?via%3Dihub

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