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The stop-signal task has been used to study normal cognitive control and clinical dysfunction. Its utility is derived from a race model that accounts for performance and provides an estimate of the time it takes to stop a movement. This model posits a race between go and stop processes with stochastically independent finish times. However, neurophysiological studies demonstrate that the neural correlates of the go and stop processes produce movements through a network of interacting neurons. The juxtaposition of the computational model with the neural data exposes a paradox-how can a network of interacting units produce behavior that appears to be the outcome of an independent race? The authors report how a simple, competitive network can solve this paradox and provide an account of what is measured by stop-signal reaction time.(PsycINFO Database Record (c) 2016 APA, all rights reserved)