Countermanding an action is a fundamental form of cognitive control. In a saccade-countermanding task, subjects are instructed that, if a stop signal appears shortly after a target, they are to maintain fixation rather than to make a saccade to the target. In recent years, recordings in the frontal eye fields and superior colliculus of behaving non-human primates have found correlates of such countermanding behavior in movement and fixation neurons. In this work, we extend a previous neural network model of countermanding to account for the high pre-target activity of fixation neurons. We propose that this activity reflects the functioning of control mechanisms responsible for optimizing performance. We demonstrate, using computer simulations and mathematical analysis, that pre-target fixation neuronal activity supports countermanding behavior that maximizes reward rate as a function of the stop signal delay, fraction of stop signal trials, intertrial interval, duration of timeout, and relative reward value. We propose experiments to test these predictions regarding optimal behavior.
Published by Elsevier B.V.