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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关于规则引导的行为如何变得自动化的神经计算理论。

本文介绍了一个关于规则引导行为如何变得自动化的生物学详细计算模型。该模型假设最初，规则引导的行为由以前额叶皮层为中心的分布式神经网络控制，除了发起行为外，该网络还训练一个更快、更直接的网络，其中包括从感觉关联皮层直接到前运动皮层中的规则敏感神经元。经过多次练习，直接网络足以控制行为，无需前额参与。该模型被实现为由尖峰神经元构建的生物学详细神经网络，并显示出生物学上合理的 Hebbian 学习形式。该模型成功地解释了其他自动化模型存在问题的单单元记录和人类行为数据。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。