A brain-behavior generative model links individual brain dynamics to perceptual decisions

Abstract

Perceptual decision speed varies across individuals and trials. Explaining this variability requires linking individual brain anatomy, neural dynamics, and behavior, yet whole-brain models typically reproduce brain signals without behavior, whereas behavioral models lack anatomical constraints. Here we introduce a brain-behavior generative model in which whole-brain dynamics on each participant's structural connectome generate both single-trial electroencephalography (EEG) and reaction times. Fitted to 17 participants performing motion discrimination, the models reproduced individual event-related potentials and reaction-time distributions. Joint fitting enabled the models to predict whether held-out trials were relatively fast or slow within each stimulus condition. In silico silencing further predicted where, how, and when each person's brain controls decision speed. Sensorimotor regions affected decision speed consistently across most participants, whereas the same parietal region had opposite effects in different participants. These opposite effects reflected whether the region acted mainly through network dynamics or the decision readout. Temporal analyses suggested that early activity largely set decision speed, whereas late EEG mainly tracked the unfolding decision. These results show that brain-behavior generative modeling can link individual brain dynamics to perceptual decisions and yield testable predictions of why decision speed varies across individuals and trials.