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人类脑电图在学习过程中揭示潜在的可推广的规则结构。

Human EEG uncovers latent generalizable rule structure during learning.

机构信息

Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island 02912, and Department of Psychology, University of New Mexico, Albuquerque, New Mexico 87131.

出版信息

J Neurosci. 2014 Mar 26;34(13):4677-85. doi: 10.1523/JNEUROSCI.3900-13.2014.

Abstract

Human cognition is flexible and adaptive, affording the ability to detect and leverage complex structure inherent in the environment and generalize this structure to novel situations. Behavioral studies show that humans impute structure into simple learning problems, even when this tendency affords no behavioral advantage. Here we used electroencephalography to investigate the neural dynamics indicative of such incidental latent structure. Event-related potentials over lateral prefrontal cortex, typically observed for instructed task rules, were stratified according to individual participants' constructed rule sets. Moreover, this individualized latent rule structure could be independently decoded from multielectrode pattern classification. Both neural markers were predictive of participants' ability to subsequently generalize rule structure to new contexts. These EEG dynamics reveal that the human brain spontaneously constructs hierarchically structured representations during learning of simple task rules.

摘要

人类认知具有灵活性和适应性,能够检测和利用环境中固有的复杂结构,并将这种结构推广到新的情境中。行为研究表明,即使这种趋势没有带来行为上的优势,人类也会将结构推断到简单的学习问题中。在这里,我们使用脑电图来研究表明这种偶然潜在结构的神经动力学。与指令任务规则相关的外侧前额叶皮层的事件相关电位(ERP)根据个体参与者构建的规则集进行分层。此外,这种个体化的潜在规则结构可以通过多电极模式分类来独立解码。这两个神经标记都可以预测参与者随后将规则结构推广到新情境的能力。这些 EEG 动力学表明,在简单任务规则的学习过程中,人类大脑会自发地构建层次化的表示。

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