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为新任务组装旧技巧:一个指令学习和控制的神经模型。

Assembling old tricks for new tasks: a neural model of instructional learning and control.

机构信息

University of Colorado, Boulder, CO 80309, USA.

出版信息

J Cogn Neurosci. 2013 Jun;25(6):843-51. doi: 10.1162/jocn_a_00365. Epub 2013 Feb 5.

Abstract

We can learn from the wisdom of others to maximize success. However, it is unclear how humans take advice to flexibly adapt behavior. On the basis of data from neuroanatomy, neurophysiology, and neuroimaging, a biologically plausible model is developed to illustrate the neural mechanisms of learning from instructions. The model consists of two complementary learning pathways. The slow-learning parietal pathway carries out simple or habitual stimulus-response (S-R) mappings, whereas the fast-learning hippocampal pathway implements novel S-R rules. Specifically, the hippocampus can rapidly encode arbitrary S-R associations, and stimulus-cued responses are later recalled into the basal ganglia-gated pFC to bias response selection in the premotor and motor cortices. The interactions between the two model learning pathways explain how instructions can override habits and how automaticity can be achieved through motor consolidation.

摘要

我们可以从他人的智慧中学习,以最大限度地取得成功。然而,目前尚不清楚人类如何接受建议,从而灵活地调整行为。基于神经解剖学、神经生理学和神经影像学的数据,我们开发了一个具有生物学合理性的模型,以阐明从指令中学习的神经机制。该模型由两个互补的学习途径组成。慢速学习的顶叶途径执行简单或习惯性的刺激-反应(S-R)映射,而快速学习的海马途径则实施新的 S-R 规则。具体来说,海马体可以快速编码任意的 S-R 关联,并且受到刺激提示的反应随后被回忆到基底神经节门控的前额叶皮层中,以偏向于在前运动和运动皮层中的反应选择。这两个模型学习途径的相互作用解释了指令如何能够覆盖习惯,以及自动性如何通过运动巩固来实现。

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