Individual differences in dynamic criterion shifts during perceptual decision-making

Abstract

Perceptual decision-making involves placing an optimal criterion on the axis of encoded sensory evidence to maximize outcomes for choices. Optimal criterion setting becomes critical particularly when neural representations of sensory inputs are noisy and feedbacks for perceptual choices vary over time in an unpredictable manner. Here, we first induced shifts in criteria by adopting stochastically generated reverse feedbacks, so that subjects sometimes received false feedbacks even for responses they should have gotten correct. Using a reinforcement-learning model, we captured subjects behavior and we investigated rationale for optimal criteria placement, or lack of, in individuals. Utilizing the estimates of model parameters, specifically the shift rate for criteria and subjects sensitivity, we identified the optimal regime. We demonstrate that optimal decision-making is closely correlated with the subjects sensitivity and shift rate. There was a systematic trade-off between the two parameters indicating that sensitivity and shift rate reciprocate, allowing for optimal decision-making in complex environments.