Hanrui Mei
Department of Psychology
The Ohio State University
Title: Comparing Diffusion model and accumulator model with cross-fitting method
Abstract: Sequential sampling models are widely used to predict both response time (RT) and choice probability data in behavioral decision tasks. Different models in this domain may have different assumptions about how information is accumulated in cognitive processes. For example, diffusion models assume that evidence accumulation for two choice alternatives is one single process, while accumulator or racing models accumulate the evidence for two alternative responses as two separate diffusion processes. However, these models can exhibit model mimicry, where they perform similarly well in predicting behavioral data despite their different underlying assumptions. In this study, we employ both data-informed and data-uninformed cross-fitting methods to examine model mimicry between the diffusion model and the racing model. The cross-fitting method generates data from one model and fits it to the other model. If the second model should fit the data well if it could capture the pattern of data generated from the first model.
Discussant: Shannon Jacoby