Work

Computational and motivational mechanisms of human social decision making involving close others

Year: 2021

Type: article

Abstract: Every day, human beings make decisions with social consequences. These social consequences matter most when they impact those closest to us. Recent research has shown that humans exhibit reliable preferences when deciding between conflicting outcomes involving close others – for example, prioritizing the interests of one’s family member over one’s friend. However, virtually nothing is known about the mechanisms that drive these preferences. We conducted a pre-registered study in a large (N = 375) sample to quantify the computational and motivational mechanisms of human social decision making preferences involving close others. By pairing assessment techniques from behavioral economics and psychological science with computational modeling and random coefficient regression, we show that value-based cognitive computations (e.g., risk and loss aversion) drive social decision making preferences involving financial outcomes, whereas socioemotional motivations (e.g., relationship quality) underlie preferences involving social outcomes. These results imply mechanistic heterogeneity, underscoring a need for greater attention to contextual specificity in social decision making.

Source: Journal of Experimental Social Psychology

DOI: 10.1016/j.jesp.2020.104086

Authors: João F. Guassi Moreira, Sarah M. Tashjian, Adriana Galván, Jennifer A. Silvers

Institutions: University of California, Los Angeles, California Institute of Technology

Language: English

Topic: Decision-Making and Behavioral Economics

Subfield: General Decision Sciences

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Award: Doctoral Training in Brain and Behavioral Development During Adolescence (UCLA BBDA)