Psychology Research and Behavior Management (Jun 2024)
Computational Modeling Interpretation Underlying Elevated Risk-Taking Propensity in the Dynamic Risky Investment Process of Non-Labor Income
Abstract
Yuanyuan Hu,1,2,* Yuening Jin,1,2,* Bowen Hu,3 Tingyong Feng,3 Yuan Zhou1,2,4 1CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, People’s Republic of China; 2Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China; 3Faculty of Psychology, Southwest University, Chongqing, 400715, People’s Republic of China; 4The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100120, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuan Zhou, Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing, 100101, People’s Republic of China, Email [email protected] Tingyong Feng, Faculty of Psychology, Southwest University, No. 2, Tian Sheng Road, Beibei, ChongQing, 400715, People’s Republic of China, Email [email protected]: Money source influences risk-taking behaviors. Although studies consistently indicated that individuals demonstrate a higher propensity to make risky investments when utilizing non-labor income as opposed to labor income, explanations as to why non-labor income leads to continuously blowing money into risky investments are scarce.Methods: The current study leverages a computational modeling approach to compare the differences in the dynamic risk investment process among individuals endowed with income from different sources (ie, non-labor income vs labor income) to understand the shaping force of higher risk-taking propensity in individuals with non-labor income. A total of 103 participants were recruited and completed the Balloon Analogue Risk Task (BART) with an equal monetary endowment, either as a token for completion of survey questionnaires (representing labor income) or as a prize from a lucky draw game (representing non-labor income).Results: We found that individuals endowed with non-labor income made more risky investments in BART compared to those with labor income. With computational modeling, we further identified two key differences in the dynamic risk investment processes between individuals endowed with labor and those with non-labor income. Specifically, individuals endowed with non-labor income had a higher preset expectation for risk-taking and displayed desensitization towards losses during risk investments, in contrast to individuals with labor income.Discussion: This study contributed to a better understanding of the psychological mechanisms of why individuals make more risk-taking behaviors with non-labor income, namely higher preset expectations of risk-taking and desensitization towards losses. Future research could validate these findings across diverse samples with varying backgrounds and adopt different manipulations of labor and non-labor income to enhance the external validity of our study.Keywords: non-labor income, balloon analogue risk task, computational modeling, hierarchical Bayesian analysis, reinforcement learning
