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基于大脑的“基本层次”概念解释。

A brain-based account of "basic-level" concepts.

机构信息

Sidney Smith Hall, Dept. of Psychology, University of Toronto, 100 St. George Street, Toronto, ON, M5S 3G3, Canada.

Center for Cognitive Brain Imaging, Baker Hall, Dept. of Psychology, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.

出版信息

Neuroimage. 2017 Nov 1;161:196-205. doi: 10.1016/j.neuroimage.2017.08.049. Epub 2017 Aug 19.

DOI:10.1016/j.neuroimage.2017.08.049
PMID:28826947
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5696099/
Abstract

This study provides a brain-based account of how object concepts at an intermediate (basic) level of specificity are represented, offering an enriched view of what it means for a concept to be a basic-level concept, a research topic pioneered by Rosch and others (Rosch et al., 1976). Applying machine learning techniques to fMRI data, it was possible to determine the semantic content encoded in the neural representations of object concepts at basic and subordinate levels of abstraction. The representation of basic-level concepts (e.g. bird) was spatially broad, encompassing sensorimotor brain areas that encode concrete object properties, and also language and heteromodal integrative areas that encode abstract semantic content. The representation of subordinate-level concepts (robin) was less widely distributed, concentrated in perceptual areas that underlie concrete content. Furthermore, basic-level concepts were representative of their subordinates in that they were neurally similar to their typical but not atypical subordinates (bird was neurally similar to robin but not woodpecker). The findings provide a brain-based account of the advantages that basic-level concepts enjoy in everyday life over subordinate-level concepts: the basic level is a broad topographical representation that encompasses both concrete and abstract semantic content, reflecting the multifaceted yet intuitive meaning of basic-level concepts.

摘要

本研究提供了一个基于大脑的解释,说明中间(基本)特定水平的物体概念是如何被表示的,为一个概念是基本水平概念的含义提供了更丰富的视角,这是 Rosch 等人(Rosch 等人,1976 年)开创的研究课题。将机器学习技术应用于 fMRI 数据,我们可以确定基本和从属抽象水平的物体概念的神经表示中所编码的语义内容。基本水平概念(例如鸟类)的表示是广泛的,包括编码具体物体属性的感觉运动大脑区域,以及编码抽象语义内容的语言和异模态综合区域。从属水平概念(例如知更鸟)的表示则分布较窄,集中在基础具体内容的感知区域。此外,基本水平的概念代表了它们的下属概念,因为它们在神经上与典型而非典型的下属概念相似(鸟类在神经上与知更鸟相似,而不是与啄木鸟相似)。这些发现提供了一个基于大脑的解释,说明基本水平的概念在日常生活中相对于从属水平的概念具有优势:基本水平是一个广泛的地形表示,包括具体和抽象的语义内容,反映了基本水平概念的多方面但直观的含义。

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