Automatic biases in intertemporal choice
Dual process theories of intertemporal decision making propose that decision makers automatically favor immediate rewards. In this paper, we use a drift diffusion model to implement these theories, and empirically investigate the role of their proposed automatic biases. Our model permits automatic biases in the response process, in the form of a shifted starting point, as well as automatic biases in the evaluation process, in the form of an additive drift rate intercept. We fit our model to individual-level choice and response time data, and find that automatic biases (as measured though the starting point and drift rate intercept in our model) are prevalent in intertemporal choice, but that the type, magnitude, and direction of these biases vary greatly across individuals. Our results pose new challenges for theories of intertemporal choice behavior.
KeywordsDrift diffusion model Intertemporal choice Computational modelling Automatic bias Dual process theories
A. Diederich received funding from Deutsche Forschungsgemeinschaft DI506/14-1 and DI506/15-1. J. S. Trueblood was supported by National Science Foundation Grant SES-1556325 and by the Alfred P. Sloan Foundation. S. Bhatia received funding from the National Science Foundation grant SES-1626825.
- Diederich, A., & Trueblood, J. S. (2018). A dynamic dual process model of risky decision making. Psychological review, 125(2), 270.Google Scholar
- Shamosh, N. A., DeYoung, C. G., Green, A. E., Reis, D. L., Johnson, M. R., Conway, A. R., ... Gray, J. R. (2008). Individual differences in delay discounting: relation to intelligence, working memory, and anterior prefrontal cortex. Psychological Science, 19(9), 904-911.CrossRefPubMedPubMedCentralGoogle Scholar
- Townsend, J. T., & Ashby, F. G. (1983). Stochastic modeling of elementary psychological processes. CUP Archive.Google Scholar
- Wiecki, T. V., Sofer, I., & Frank, M. J. (2013). HDDM: Hierarchical Bayesian estimation of the drift-diffusion model in Python. Frontiers in Neuroinformatics, 7Google Scholar