默认模式 "网络如何促进语义认知?

IF 2.1 2区 心理学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY
Leonardo Fernandino , Jeffrey R. Binder
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引用次数: 0

摘要

本综述探讨了 "默认模式 "网络(DMN)是否以及如何促进语义处理。我们回顾了 DMN 与单个词义的处理以及句子和话语层面的语义学有关的证据。接下来,我们论证了组成DMN的区域通过协调和整合多个单模态和多模态皮层区域的局部神经元集合的同时活动,形成一个瞬时的全局神经元集合,从而促进语义处理。由此产生的神经元集合实现了对现象学体验的综合模拟,也就是由各种模式的体验记忆痕迹构建的具身情境模型。我们认为,这些情境模型不仅对语义处理是必要的,而且对传统上不被视为语义的认知方面也是必要的。尽管这一提议的许多方面仍是临时性的,但我们相信它为语义认知与非语义认知之间的关系以及DMN的功能提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How does the “default mode” network contribute to semantic cognition?

This review examines whether and how the “default mode” network (DMN) contributes to semantic processing. We review evidence implicating the DMN in the processing of individual word meanings and in sentence- and discourse-level semantics. Next, we argue that the areas comprising the DMN contribute to semantic processing by coordinating and integrating the simultaneous activity of local neuronal ensembles across multiple unimodal and multimodal cortical regions, creating a transient, global neuronal ensemble. The resulting ensemble implements an integrated simulation of phenomenological experience – that is, an embodied situation model – constructed from various modalities of experiential memory traces. These situation models, we argue, are necessary not only for semantic processing but also for aspects of cognition that are not traditionally considered semantic. Although many aspects of this proposal remain provisional, we believe it provides new insights into the relationships between semantic and non-semantic cognition and into the functions of the DMN.

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来源期刊
Brain and Language
Brain and Language 医学-神经科学
CiteScore
4.50
自引率
8.00%
发文量
82
审稿时长
20.5 weeks
期刊介绍: An interdisciplinary journal, Brain and Language publishes articles that elucidate the complex relationships among language, brain, and behavior. The journal covers the large variety of modern techniques in cognitive neuroscience, including functional and structural brain imaging, electrophysiology, cellular and molecular neurobiology, genetics, lesion-based approaches, and computational modeling. All articles must relate to human language and be relevant to the understanding of its neurobiological and neurocognitive bases. Published articles in the journal are expected to have significant theoretical novelty and/or practical implications, and use perspectives and methods from psychology, linguistics, and neuroscience along with brain data and brain measures.
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