Joint computational modeling of human EEG and behavior reveal individual differences in cognition during perceptual 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 reveal additional individual differences in cognition. In particular it is thought that the collection of EEG data can reveal individual differences in visual attention, visual encoding time (VET), and evidence accumulation paths. We discuss our recent efforts to verify cognitive interpretations of EEG potentials and NDDM parameters in a preregistered study. In particular, we show evidence that individual differences in the onset of evidence accumulation can be measured, but show mixed evidence for understanding individual differences in evidence accumulation paths. Through this work we have discovered best practices for joint computational modeling of human EEG and behavior and make suggestions for the future of similar work.