Choice-Induced Preference Change under a Sequential Sampling Model Framework

Sequential sampling models of choice, such as the drift-diffusion model (DDM), are frequently fit to empirical data to account for a variety of effects related to accuracy/consistency, response time (RT), and sometimes confidence. However, no model in this class has been shown to account for the phenomenon known as choice-induced preference change , wherein decision makers tend to rate options higher after they choose them and lower after they reject them (and often choose the option that they had initially rated lower). Studies have reported choice-induced preference change for many decades, and the principal findings are robust. The resulting spreading of alternatives (SoA) in terms of their subjective value ratings is incompatible with traditional sequential sampling models, which consider the rated values of the options to be stationary throughout choice deliberation. Here, we propose that extending the basic DDM to incorporate independent attributes into the drift rate can allow this class of model to account for SoA. Critically, the extended model assumes that choice deliberation does not always involve the same set of attributes as is involved in individual evaluations of the same options. We show that this model can generate SoA (while simultaneously accounting for consistency and RT), as well as the relationships between SoA and choice difficulty, attribute disparity, and RT previously reported in the literature.