Recovering parameters of joint models of human EEG and behavior during decision making

Fitting drift-diffusion models (DDMs) to multiple participants’ choices and response times during perceptual decision making tasks result in parameter estimates that have cognitive interpretations such as individual differences in speed-accuracy tradeoffs and the average rates of evidence accumulation. The cognitive interpretations of DDM parameters can then be verified with experimental conditions and manipulations. Fitting neural drift-diffusion models (NDDMs) to participants’ scalp-recorded EEG as well as choices and response times can further reveal additional individual differences in cognition, such as individual differences in visual attention, visual encoding time (VET), and evidence accumulation processes. We discuss our recent efforts to develop NDDMs that are useful in understanding differences across individuals. In particular we are interested in models that actually recover parameters from simulated behavior and EEG data. Often newly developed NDDMs converge to a solution when using hierarchical Bayesian methods. However, whether the posterior distributions of parameters are informative about individual differences is not clear unless parameter recovery and parameter generalization to similar models are confirmed. In particular we discuss modeling efforts to understand individual differences in cognition that cannot be learned with models of either EEG or behavior alone.