Department of Psychology, University of Victoria, P. O. Box 1700 STN CSC, Victoria, British Columbia, V8W 2Y2, Canada.
Psychon Bull Rev. 2018 Feb;25(1):302-321. doi: 10.3758/s13423-017-1280-1.
Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function.
前扣带皮层（ACC）在过去的 20 年里一直是激烈争论的主题，但它的具体计算功能仍存在争议。在这里，我们提出了一个简单的 ACC 计算模型，该模型结合了相互连接的处理单元网络中的分布式表示。基于 ACC 与扩展的、有目标导向的动作序列的执行有关的假设，我们训练了一个递归神经网络来预测与多个任务相关的几个序列的后续步骤。与非人类动物的神经生理学观察结果一致，该网络在 ACC 神经元中产生了分布式的活动模式，这些模式跟踪每个序列的进展，与人类神经影像学数据一致，当序列的任何一步偏离预测的步骤时，网络会产生差异信号。这些模拟说明了一种研究 ACC 功能的新方法。
