Fernandino Leonardo, Humphries Colin J, Conant Lisa L, Seidenberg Mark S, Binder Jeffrey R
Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, and
Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, and.
J Neurosci. 2016 Sep 21;36(38):9763-9. doi: 10.1523/JNEUROSCI.4095-15.2016.
The capacity to process information in conceptual form is a fundamental aspect of human cognition, yet little is known about how this type of information is encoded in the brain. Although the role of sensory and motor cortical areas has been a focus of recent debate, neuroimaging studies of concept representation consistently implicate a network of heteromodal areas that seem to support concept retrieval in general rather than knowledge related to any particular sensory-motor content. We used predictive machine learning on fMRI data to investigate the hypothesis that cortical areas in this "general semantic network" (GSN) encode multimodal information derived from basic sensory-motor processes, possibly functioning as convergence-divergence zones for distributed concept representation. An encoding model based on five conceptual attributes directly related to sensory-motor experience (sound, color, shape, manipulability, and visual motion) was used to predict brain activation patterns associated with individual lexical concepts in a semantic decision task. When the analysis was restricted to voxels in the GSN, the model was able to identify the activation patterns corresponding to individual concrete concepts significantly above chance. In contrast, a model based on five perceptual attributes of the word form performed at chance level. This pattern was reversed when the analysis was restricted to areas involved in the perceptual analysis of written word forms. These results indicate that heteromodal areas involved in semantic processing encode information about the relative importance of different sensory-motor attributes of concepts, possibly by storing particular combinations of sensory and motor features.
The present study used a predictive encoding model of word semantics to decode conceptual information from neural activity in heteromodal cortical areas. The model is based on five sensory-motor attributes of word meaning (color, shape, sound, visual motion, and manipulability) and encodes the relative importance of each attribute to the meaning of a word. This is the first demonstration that heteromodal areas involved in semantic processing can discriminate between different concepts based on sensory-motor information alone. This finding indicates that the brain represents concepts as multimodal combinations of sensory and motor representations.
以概念形式处理信息的能力是人类认知的一个基本方面，但对于这类信息在大脑中是如何编码的，我们却知之甚少。尽管感觉和运动皮层区域的作用一直是近期争论的焦点，但概念表征的神经影像学研究始终表明，存在一个异模态区域网络，该网络似乎总体上支持概念检索，而非与任何特定感觉运动内容相关的知识。我们对功能磁共振成像（fMRI）数据使用了预测性机器学习，以研究这样一个假设：这个“一般语义网络”（GSN）中的皮层区域编码源自基本感觉运动过程的多模态信息，可能作为分布式概念表征的汇聚-发散区发挥作用。一个基于与感觉运动体验直接相关的五个概念属性（声音、颜色、形状、可操作性和视觉运动）的编码模型，被用于预测在语义决策任务中与各个词汇概念相关的大脑激活模式。当分析仅限于GSN中的体素时，该模型能够显著高于随机水平地识别与各个具体概念相对应的激活模式。相比之下，一个基于单词形式的五个感知属性的模型表现处于随机水平。当分析仅限于参与书面单词形式感知分析的区域时，这种模式则相反。这些结果表明，参与语义处理的异模态区域编码有关概念不同感觉运动属性相对重要性的信息，可能是通过存储感觉和运动特征的特定组合来实现的。
本研究使用单词语义的预测编码模型，从异模态皮层区域的神经活动中解码概念信息。该模型基于单词意义的五个感觉运动属性（颜色、形状、声音、视觉运动和可操作性），并编码每个属性对单词意义的相对重要性。这是首次证明参与语义处理的异模态区域能够仅基于感觉运动信息区分不同概念。这一发现表明，大脑将概念表征为感觉和运动表征的多模态组合。
