This article introduces a biologically detailed computational model of how rule-guided behaviors become automatic. The model assumes that initially, rule-guided behaviors are controlled by a distributed neural network centered in the prefrontal cortex, and that in addition to initiating behavior, this network also trains a faster and more direct network that includes projections from sensory association cortex directly to rule-sensitive neurons in the premotor cortex. After much practice, the direct network is sufficient to control the behavior, without prefrontal involvement. The model is implemented as a biologically detailed neural network constructed from spiking neurons and displaying a biologically plausible form of Hebbian learning. The model successfully accounts for single-unit recordings and human behavioral data that are problematic for other models of automaticity. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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关于规则引导行为如何自动化的神经计算理论。

本文介绍了生物学上详细的计算模型，该模型介绍了规则指导的行为如何自动实现。该模型假定，最初，规则指导的行为由位于前额叶皮层中心的分布式神经网络控制，并且除了启动行为外，该网络还训练了一个更快，更直接的网络，其中包括从感觉关联皮层直接投射到运动前皮层中的规则敏感神经元。经过大量实践，直接网络足以控制行为，而无需前额介入。该模型被实现为一个生物详细的神经网络，该神经网络由尖刺的神经元构建而成，并显示出生物学上合理的Hebbian学习形式。该模型成功地解决了单单位记录和人类行为数据，这对于其他自动化模型是有问题的。（PsycInfo数据库记录（c）2021 APA，保留所有权利）。