Multifaceted adaptation of the neural decision process with prior knowledge of time constraints and stimulus probability

Abstract

When selecting actions in response to noisy sensory stimuli, the brain can exploit prior knowledge of time constraints, stimulus discriminability and stimulus probability to hone the decision process. Although behavioral models typically explain such effects through adjustments to decision criteria only, the full range of underlying neural process adjustments remains to be established. Here, we draw on human neurophysiological signals reflecting decision formation to construct and constrain a multi-tiered model of prior-informed motion discrimination, in which a motor-independent representation of cumulative evidence feeds build-to-threshold motor signals that receive additional dynamic urgency and bias signal components. The neurally-informed model not only provides a superior quantitative fit to prior-biased behavior across three distinct task regimes (easy, time-pressured and weak evidence), but also reveals adjustments to evidence accumulation rate, urgency rate, and the timing of accumulation onset and motor execution which go undetected or are discrepant in more standard diffusion-model analysis of behavior.