Yang Ying, Dickey Michael Walsh, Fiez Julie, Murphy Brian, Mitchell Tom, Collinger Jennifer, Tyler-Kabara Elizabeth, Boninger Michael, Wang Wei
Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA; School of Electronics, Electrical Engineering and Computer Science, Queen's University, Belfast, United Kingdom.
Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA; Geriatric Research Education and Clinical Center, V.A. Pittsburgh Healthcare System, Pittsburgh, PA, USA; Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, USA.
Cortex. 2017 Jul;92:304-319. doi: 10.1016/j.cortex.2017.04.021. Epub 2017 May 6.
Semantic grounding is the process of relating meaning to symbols (e.g., words). It is the foundation for creating a representational symbolic system such as language. Semantic grounding for verb meaning is hypothesized to be achieved through two mechanisms: sensorimotor mapping, i.e., directly encoding the sensorimotor experiences the verb describes, and verb-category mapping, i.e., encoding the abstract category a verb belongs to. These two mechanisms were investigated by examining neuronal-level spike (i.e. neuronal action potential) activities from the motor, somatosensory and parietal areas in two human participants. Motor and a portion of somatosensory neurons were found to be involved in primarily sensorimotor mapping, while parietal and some somatosensory neurons were found to be involved in both sensorimotor and verb-category mapping. The time course of the spike activities and the selective tuning pattern of these neurons indicate that they belong to a large neural network used for semantic processing. This study is the first step towards understanding how words are processed by neurons.
语义基础是将意义与符号（如单词）相关联的过程。它是创建诸如语言之类的表征符号系统的基础。动词意义的语义基础被假定通过两种机制实现：感觉运动映射，即直接编码动词所描述的感觉运动体验，以及动词类别映射，即编码动词所属的抽象类别。通过检查两名人类参与者运动、体感和顶叶区域的神经元水平的尖峰（即神经元动作电位）活动，对这两种机制进行了研究。发现运动神经元和一部分体感神经元主要参与感觉运动映射，而顶叶神经元和一些体感神经元则参与感觉运动映射和动词类别映射。尖峰活动的时间进程以及这些神经元的选择性调谐模式表明，它们属于一个用于语义处理的大型神经网络。这项研究是迈向理解神经元如何处理单词的第一步。
