Choice-Induced Preference Change under a Sequential Sampling Model Framework

Abstract

AbstractSequential 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 choice accuracy/consistency and response time (RT). Sometimes, these models include extensions that can also account for choice confidence. However, no model in this class is able to account for the phenomenon ofchoice-induced preference change. Studies have reported choice-induced preference change for many decades, and the principle findings are robust: decision-makers tend to rate options higher after they choose them and lower after they reject them. Thisspreading of alternatives(SoA) in terms of their rated values is fundamentally incompatible with traditional sequential sampling models, which consider the rated values of the options to be stationary throughout choice deliberation. Here, we propose a simple modification of the basic DDM that allows the drift rate to vary across deliberation time depending on which attributes are attended to at which points in time. Critically, the model assumes that initial ratings are based only on the more salient attributes of the individual options, and that more attributes will be considered when decision-makers must choose between options with different salient attributes. We show that this model can account for SoA (in addition to choice consistency and RT), as well as all previously reported relationships between SoA and choice difficulty, attribute disparity, and RT.