A unified model of human semantic knowledge and its disorders
A unified model of human semantic knowledge and its disorders
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DOI：
10.1038/s41562-016-0039
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发表时间：
2017-03-01
影响因子：
29.9
通讯作者：
Rogers, Timothy T.
中科院分区：
文献类型：
--
作者：
Chen, Lang;Ralph, Matthew A. Lambon;Rogers, Timothy T.
关于单词和物体的含义的知识是如何在人脑中表现出来的？目前的理论包含两种截然不同的假设：要么是不同的大脑皮层系统进化为代表不同种类的事物，要么是所有种类的知识都编码在一个单一的领域--通用网络中。这两种观点都不能全面解释神经成像和神经心理学的相关证据。本文提出，通过学习和网络连通性的共同作用，在语义网络的某些成分中出现了分级的类别专用性。我们通过测量语义表示所涉及的大脑皮层区域之间的连通性来测试该建议，然后在其体系结构反映了这种结构的深层神经网络中模拟健康和无序的语义处理。由此产生的神经计算模型解释了神经成像和患者证据的全部补充，以支持特定领域和通用领域的方法，调和了关于这种独特的人类认知能力的性质和起源的长期争论。
How is knowledge about the meanings of words and objects represented in the human brain? Current theories embrace two radically different proposals: either distinct cortical systems have evolved to represent different kinds of things, or knowledge for all kinds is encoded within a single domain-general network. Neither view explains the full scope of relevant evidence from neuroimaging and neuropsychology. Here we propose that graded category-specificity emerges in some components of the semantic network through joint effects of learning and network connectivity. We test the proposal by measuring connectivity amongst cortical regions implicated in semantic representation, then simulating healthy and disordered semantic processing in a deep neural network whose architecture mirrors this structure. The resulting neuro-computational model explains the full complement of neuroimaging and patient evidence adduced in support of both domain-specific and domain-general approaches, reconciling long-standing disputes about the nature and origins of this uniquely human cognitive faculty.