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Representational Similarity Analysis Reveals Commonalities and Differences in the Semantic Processing of Words and Objects

机译:代表性相似性分析揭示了单词和对象的语义处理中的共性和差异

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摘要

Understanding the meanings of words and objects requires the activation of underlying conceptual representations. Semantic representations are often assumed to be coded such that meaning is evoked regardless of the input modality. However, the extent to which meaning is coded in modality-independent or amodal systems remains controversial. We address this issue in a human fMRI study investigating the neural processing of concepts, presented separately as written words and pictures. Activation maps for each individual word and picture were used as input for searchlight-based multivoxel pattern analyses. Representational similarity analysis was used to identify regions correlating with low-level visual models of the words and objects and the semantic category structure common to both. Common semantic category effects for both modalities were found in a left-lateralized network, including left posterior middle temporal gyrus (LpMTG), left angular gyrus, and left intraparietal sulcus (LIPS), in addition to object- and word-specific semantic processing in ventral temporal cortex and more anterior MTG, respectively. To explore differences in representational content across regions and modalities, we developed novel data-driven analyses, based on k-means clustering of searchlight dissimilarity matrices and seeded correlation analysis. These revealed subtle differences in the representations in semantic-sensitive regions, with representations in LIPS being relatively invariant to stimulus modality and representations in LpMTG being uncorrelated across modality. These results suggest that, although both LpMTG and LIPS are involved in semantic processing, only the functional role of LIPS is the same regardless of the visual input, whereas the functional role of LpMTG differs for words and objects.
机译:理解单词和对象的含义需要激活基础的概念表示。通常假定语义表示已编码,因此无论输入形式如何,都会引起含义。但是,在与情态无关或无情的系统中编码含义的程度仍存在争议。我们在一项人类fMRI研究中解决了这个问题,该研究调查了概念的神经处理,以书面文字和图片的形式分别呈现。每个单词和图片的激活图都用作基于探照灯的多体素模式分析的输入。代表性相似性分析用于识别与单词和对象的低级视觉模型以及两者共同的语义类别结构相关的区域。在左偏侧化网络中发现了两种模式的常见语义类别效应,包括左后中颞颞回(LpMTG),左角回和左顶壁沟(LIPS),以及腹颞叶皮层和更前MTG。为了探索跨区域和模态的代表性内容的差异,我们基于探照灯相异性矩阵的k均值聚类和种子相关分析,开发了新颖的数据驱动型分析。这些揭示了语义敏感区域中表示形式的细微差异,其中LIPS中的表示形式相对于刺激模态相对不变,而LpMTG中的表示形式在整个模态之间不相关。这些结果表明,尽管LpMTG和LIPS都参与了语义处理,但是无论视觉输入如何,只有LIPS的功能角色是相同的,而单词和对象的LpMTG的功能角色却有所不同。

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