Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544, USA.
Brain Res. 2010 Mar 8;1318:178-87. doi: 10.1016/j.brainres.2009.12.018. Epub 2009 Dec 23.
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.
撤销动作是认知控制的一种基本形式。在眼跳撤销任务中，会指示被试，如果在目标出现后不久出现停止信号，则他们应保持注视，而不是将视线转移到目标上。近年来，对行为非人类灵长类动物的额眼区和上丘的记录发现，运动和固定神经元中存在与这种撤销行为相关的关联。在这项工作中，我们扩展了以前的撤销神经网络模型，以解释固定神经元的高前目标活动。我们提出，这种活动反映了负责优化性能的控制机制的作用。我们使用计算机模拟和数学分析证明，前目标固定神经元活动支持最大化奖励率的撤销行为，其奖励率是停止信号延迟、停止信号试验比例、试验间间隔、超时持续时间和相对奖励值的函数。我们提出了实验来检验这些关于最佳行为的预测。
