Assessing the validity of three tasks of risk‐taking propensity

• Published in: Journal of behavioral decision making Vol. 34; no. 4; pp. 555 - 567
• Main Authors: Zhou, Ran, Myung, Jay I., Mathews, Carol A., Pitt, Mark A.
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Periodicals Inc 01-10-2021
• Subjects: Angling Risk Task; Antiretroviral therapy; Aversion; Avoidance behavior; Balloon Analogue Risk Task; Bayesian analysis; Behavior; Cognitive ability; Cognitive models; Cognitive-behavioral factors; computational modeling; Computer applications; decision‐making under uncertainty; Discrepancies; Drug abuse; Hoarding; Hoarding behavior; Hoarding disorder; Impulsive behavior; Impulsivity; Mathematical models; Obsessive compulsive disorder; Propensity; Risk; risk‐taking propensity; Sensation seeking; Substance abuse
• ISSN: 0894-3257; 1099-0771
• DOI: 10.1002/bdm.2229

Risk‐taking propensity is a general personality disposition that has been studied using survey, behavioral, and cognitive modeling approaches, but the consistency and informativeness of the data across these approaches is rarely compared. To address this issue, we compared three behavioral tasks (BART, C‐ART, and S‐ART) designed to measure risk‐taking propensity by correlating measures from both the behavioral and modeling approaches with responses on scales assumed to relate to risk‐taking (impulsivity, sensation seeking, and drug abuse), and risk aversion (OCD symptoms and hoarding behavior). Results show that both the behavioral measure and the model parameter in the C‐ART and the S‐ART positively correlated with impulsivity and sensation seeking, with the behavioral measure showing a slight advantage. We also found discrepancies between the behavioral measure and the model parameter: the behavioral measures in the BART and the S‐ART negatively correlated with hoarding behavior, whereas the model parameter positively correlated with impulsivity and OCD symptoms. Our findings indicate that these tasks, all of which claim to measure risk‐taking propensity, vary in their abilities to measure different aspects of risk‐taking propensity. Current computational models do not yet suffice as an additional index of risk‐taking propensity.