文献类型：期刊文献

英文题名：Category representation and generalization in the prefrontal cortex

作者：Pan, Xiaochuan[1,2];Sakagami, Masamichi[1]

机构：[1]Tamagawa Univ, Brain Sci Inst, Machida, Tokyo 1948610, Japan;[2]E China Univ Sci & Techonol, Inst Cognit Neurodynam, Shanghai 200237, Peoples R China

年份：2012

卷号：35

期号：7

起止页码：1083

外文期刊名：EUROPEAN JOURNAL OF NEUROSCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000302360500010)】；

基金：This work was supported by Grant-in-aid for Scientific Research on Innovative Areas, Grant-in-aid for Scientific Research (A), and Tamagawa Global Center of Excellence Program from the Ministry of Education, Culture, Sports, Science and Technology. X. Pan is supported by National Nature Science Foundation of China (No. 10872068) and the Fundamental Research Funds for the Central Universities of China.

语种：英文

外文关键词：categorization; inference; inferotemporal cortex; monkey; reward

摘要：Categorization is a function of the brain that serves to group together items and events in our environments. Here we review the following important issues related to category representation and generalization: namely, where categories are presented in the brain, and how the brain utilizes categorical membership to generate new information. Accumulated experimental evidence shows that the prefrontal cortex (PFC) plays a critical role in category formation and generalization. We propose that prefrontal neurons abstract the commonality beyond individual stimuli, and categorize these based on their common meaning by ignoring their physical properties and learning to represent the boundaries between behaviorally significant categories. We also claim that a subgroup of prefrontal neurons simultaneously receives the category-related information and specific property information (e.g. reward) associated with an exemplar, to form a category-based representation of that property, and propagates it among stimuli of the same category, possibly reflecting a neural basis for category generalization in the PFC. These results suggest that the PFC is involved in representing abstract rules, and generating new information on the basis of previously acquired knowledge.