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 not considered by the traditional sequential sampling approach, which assumes the rated values of the options to be stationary throughout choice deliberation. Here, we propose that relaxing that assumption can allow this class of model to account for SoA. We show that the DDM 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. Even the basic DDM can reproduce some empirical results, including multi-attribute evidence is necessary for others, and allowing different start times for each attribute enables a better match with the experimental data.