默认网络、控制网络和注意力网络中的概念表示是任务相关的和跨模态的。

IF 2.1 2区 心理学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY
Philipp Kuhnke , Markus Kiefer , Gesa Hartwigsen
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引用次数: 0

摘要

概念知识是人类认知的核心。神经影像学研究表明,概念处理以任务依赖的方式涉及模态特异性和多模态大脑区域。然而,目前尚不清楚(1)概念特征表征在多大程度上也受到任务的调节,(2)多模态区域中的概念表征是否确实是跨模态的,以及(3)概念系统如何与大规模功能性脑网络相关。为了解决这些问题,我们对fMRI数据进行了多变量模式分析。40名参与者对书面单词进行了词汇判断、声音判断和动作判断三项任务。我们发现(1)概念特征表示受到任务的强烈调制,(2)几个多模态区域中的概念表示是跨模态的,(3)概念特征检索涉及默认、前顶控制和背侧注意网络。这些大规模网络中的概念表示是任务相关的和跨模态的。我们的发现支持了假设概念处理依赖于灵活、多层次架构的理论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conceptual representations in the default, control and attention networks are task-dependent and cross-modal

Conceptual knowledge is central to human cognition. Neuroimaging studies suggest that conceptual processing involves modality-specific and multimodal brain regions in a task-dependent fashion. However, it remains unclear (1) to what extent conceptual feature representations are also modulated by the task, (2) whether conceptual representations in multimodal regions are indeed cross-modal, and (3) how the conceptual system relates to the large-scale functional brain networks. To address these issues, we conducted multivariate pattern analyses on fMRI data. 40 participants performed three tasks—lexical decision, sound judgment, and action judgment—on written words. We found that (1) conceptual feature representations are strongly modulated by the task, (2) conceptual representations in several multimodal regions are cross-modal, and (3) conceptual feature retrieval involves the default, frontoparietal control, and dorsal attention networks. Conceptual representations in these large-scale networks are task-dependent and cross-modal. Our findings support theories that assume conceptual processing to rely on a flexible, multi-level architecture.

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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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