Decisions in Changing Conditions: The Urgency-Gating Model

TLDR

Decision‑making models posit that neural activity accumulates to a threshold, explaining reaction times and error rates, and are supported by neurophysiological evidence of gradual buildup linked to sensory integration. The study aims to test whether urgency gating, rather than temporal integration, drives neural buildup and decision behavior. The authors used a dynamic evidence task in which sensory input varied during each trial to differentiate urgency‑gating from temporal‑integration models. Results support urgency gating as the underlying mechanism, showing it better accounts for the data and offers a simple way to balance speed and accuracy.

Abstract

Several widely accepted models of decision making suggest that, during simple decision tasks, neural activity builds up until a threshold is reached and a decision is made. These models explain error rates and reaction time distributions in a variety of tasks and are supported by neurophysiological studies showing that neural activity in several cortical and subcortical regions gradually builds up at a rate related to task difficulty and reaches a relatively constant level of discharge at a time that predicts movement initiation. The mechanism responsible for this buildup is believed to be related to the temporal integration of sequential samples of sensory information. However, an alternative mechanism that may explain the neural and behavioral data is one in which the buildup of activity is instead attributable to a growing signal related to the urgency to respond, which multiplicatively modulates updated estimates of sensory evidence. These models are difficult to distinguish when, as in previous studies, subjects are presented with constant sensory evidence throughout each trial. To distinguish the models, we presented human subjects with a task in which evidence changed over the course of each trial. Our results are more consistent with “urgency gating” than with temporal integration of sensory samples and suggest a simple mechanism for implementing trade-offs between the speed and accuracy of decisions.