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感知和工作记忆维持过程中面部表征的时间进程

The Time Course of Face Representations during Perception and Working Memory Maintenance.

作者信息

Bae Gi-Yeul

机构信息

Department of Psychology, Arizona State University, Tempe, AZ 85287, USA.

出版信息

Cereb Cortex Commun. 2020 Dec 15;2(1):tgaa093. doi: 10.1093/texcom/tgaa093. eCollection 2021.

DOI:10.1093/texcom/tgaa093
PMID:34296148
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8152903/
Abstract

Successful social communication requires accurate perception and maintenance of invariant (face identity) and variant (facial expression) aspects of faces. While numerous studies investigated how face identity and expression information is extracted from faces during perception, less is known about the temporal aspects of the face information during perception and working memory (WM) maintenance. To investigate how face identity and expression information evolve over time, I recorded electroencephalography (EEG) while participants were performing a face WM task where they remembered a face image and reported either the identity or the expression of the face image after a short delay. Using multivariate event-related potential (ERP) decoding analyses, I found that the two types of information exhibited dissociable temporal dynamics: Although face identity was decoded better than facial expression during perception, facial expression was decoded better than face identity during WM maintenance. Follow-up analyses suggested that this temporal dissociation was driven by differential maintenance mechanisms: Face identity information was maintained in a more "activity-silent" manner compared to facial expression information, presumably because invariant face information does not need to be actively tracked in the task. Together, these results provide important insights into the temporal evolution of face information during perception and WM maintenance.

摘要

成功的社交沟通需要对面部不变(面部身份)和可变(面部表情)方面进行准确感知和维持。虽然众多研究探讨了在感知过程中如何从面部提取面部身份和表情信息,但对于感知和工作记忆(WM)维持过程中面部信息的时间方面却知之甚少。为了研究面部身份和表情信息如何随时间演变,我记录了脑电图(EEG),当时参与者正在执行一项面部WM任务,他们记住一张面部图像,并在短暂延迟后报告该面部图像的身份或表情。使用多变量事件相关电位(ERP)解码分析,我发现这两种类型的信息表现出可分离的时间动态:虽然在感知过程中面部身份比面部表情解码得更好,但在WM维持过程中面部表情比面部身份解码得更好。后续分析表明,这种时间分离是由不同的维持机制驱动的:与面部表情信息相比,面部身份信息以更“活动沉默 ”的方式维持,大概是因为在任务中不变的面部信息不需要被积极跟踪。总之,这些结果为感知和WM维持过程中面部信息的时间演变提供了重要见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7c/8152903/bf7263888cc8/tgaa093f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7c/8152903/5ddfa2fa826d/tgaa093f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7c/8152903/8b5252e9b3d8/tgaa093f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7c/8152903/a464b4394e79/tgaa093f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7c/8152903/dff9e0903aec/tgaa093f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7c/8152903/bf7263888cc8/tgaa093f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7c/8152903/5ddfa2fa826d/tgaa093f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7c/8152903/8b5252e9b3d8/tgaa093f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7c/8152903/a464b4394e79/tgaa093f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7c/8152903/dff9e0903aec/tgaa093f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e7c/8152903/bf7263888cc8/tgaa093f5.jpg

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