Ashby F Gregory, Ennis John M, Spiering Brian J
Department of Psychology, University of California, Santa Barbara, CA 93106, USA.
Psychol Rev. 2007 Jul;114(3):632-56. doi: 10.1037/0033-295X.114.3.632.
A biologically detailed computational model is described of how categorization judgments become automatic in tasks that depend on procedural learning. The model assumes 2 neural pathways from sensory association cortex to the premotor area that mediates response selection. A longer and slower path projects to the premotor area via the striatum, globus pallidus, and thalamus. A faster, purely cortical path projects directly to the premotor area. The model assumes that the subcortical path has greater neural plasticity because of a dopamine-mediated learning signal from the substantia nigra. In contrast, the cortical-cortical path learns more slowly via (dopamine independent) Hebbian learning. Because of its greater plasticity, early performance is dominated by the subcortical path, but the development of automaticity is characterized by a transfer of control to the faster cortical-cortical projection. The model, called SPEED (Subcortical Pathways Enable Expertise Development), includes differential equations that describe activation in the relevant brain areas and difference equations that describe the 2- and 3-factor learning. A variety of simulations are described, showing that the model accounts for some classic single-cell recording and behavioral results.
本文描述了一个生物学细节丰富的计算模型，该模型解释了在依赖程序性学习的任务中，分类判断是如何变得自动化的。该模型假设从感觉联合皮层到介导反应选择的运动前区存在两条神经通路。一条更长、更慢的通路通过纹状体、苍白球和丘脑投射到运动前区。一条更快的、纯皮层通路直接投射到运动前区。该模型假设，由于来自黑质的多巴胺介导的学习信号，皮层下通路具有更大的神经可塑性。相比之下，皮层-皮层通路通过（不依赖多巴胺的）赫布学习学得更慢。由于其更大的可塑性，早期表现主要由皮层下通路主导，但自动化的发展特征是控制权转移到更快的皮层-皮层投射。该模型称为SPEED（皮层下通路促进专业技能发展），包括描述相关脑区激活的微分方程和描述双因素及三因素学习的差分方程。文中描述了各种模拟，表明该模型能够解释一些经典的单细胞记录和行为结果。
