Social Sciences Event

Diffusion models are widely-used and successful accounts of the time course of two-choice decision-making. Most diffusion models assume constant boundaries, which are the threshold levels of evidence that must be sampled from a stimulus to reach a decision. We summarize theoretical results from statistics that relate distributions of decisions and response times to diffusion models with time varying boundaries. We then develop an algorithm for inferring time-varying boundaries from empirical data, and apply our new method to two problems. The first problem involves finding the time-varying boundaries that make diffusion models equivalent to the alternative sequential sampling class of accumulator models. The second problem involves inferring the time-varying bounds that best fit empirical data for stimuli that provide equal evidence for both decision alternatives. We discuss the theoretical and modeling implications of using time-varying bounds in diffusion models, as well as the limitations and potential of our approach to their inference.