Proceedings of the Annual Meeting of the Cognitive Science Society

Our goal is to develop an efficient framework for fitting stochastic continuous-time models to experimental data incognitive psychology. As a simple test problem, we consider data from an eye-tracking study of attention in learning. For eachsubject, the data for each trial consists of the sequence of stimulus features that the subject fixates on, together with the durationof each fixation. We fit a stochastic differential equation model to this data, using the Approximate Bayesian Computationframework. For each subject we infer posterior distributions for the unknown parameters in the model.