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Color associations in abstract semantic domains.
Cognition ( IF 2.8 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.cognition.2020.104306
Douglas Guilbeault 1 , Ethan O Nadler 2 , Mark Chu 3 , Donald Ruggiero Lo Sardo 4 , Aabir Abubaker Kar 5 , Bhargav Srinivasa Desikan 5
Affiliation  

The embodied cognition paradigm has stimulated ongoing debate about whether sensory data - including color - contributes to the semantic structure of abstract concepts. Recent uses of linguistic data in the study of embodied cognition have been focused on textual corpora, which largely precludes the direct analysis of sensory information. Here, we develop an automated approach to multimodal content analysis that detects associations between words based on the color distributions of their Google Image search results. Crucially, we measure color using a transformation of colorspace that closely resembles human color perception. We find that words in the abstract domains of academic disciplines, emotions, and music genres, cluster in a statistically significant fashion according to their color distributions. Furthermore, we use the lexical ontology WordNet and crowdsourced human judgments to show that this clustering reflects non-arbitrary semantic structure, consistent with metaphor-based accounts of embodied cognition. In particular, we find that images corresponding to more abstract words exhibit higher variability in colorspace, and semantically similar words have more similar color distributions. Strikingly, we show that color associations often reflect shared affective dimensions between abstract domains, thus revealing patterns of aesthetic coherence in everyday language. We argue that these findings provide a novel way to synthesize metaphor-based and affect-based accounts of embodied semantics.

中文翻译:

抽象语义域中的颜色关联。

具身认知范式引发了关于感官数据(包括颜色)是否有助于抽象概念的语义结构的持续辩论。最近语言数据在具身认知研究中的使用主要集中在文本语料库上,这在很大程度上排除了对感官信息的直接分析。在这里,我们开发了一种多模态内容分析的自动化方法,该方法根据其 Google 图片搜索结果的颜色分布来检测单词之间的关联。至关重要的是,我们使用与人类色彩感知非常相似的色彩空间转换来测量色彩。我们发现学科、情感和音乐流派的抽象领域中的单词根据它们的颜色分布以统计显着的方式聚集。此外,我们使用词汇本体 WordNet 和众包人类判断来表明这种聚类反映了非任意语义结构,与基于隐喻的具身认知描述一致。特别是,我们发现与更多抽象词相对应的图像在色彩空间中表现出更高的可变性,并且语义相似的词具有更相似的颜色分布。引人注目的是,我们表明颜色关联通常反映抽象领域之间共享的情感维度,从而揭示日常语言中审美连贯性的模式。我们认为,这些发现提供了一种新的方法来综合基于隐喻和基于情感的体现语义的解释。与基于隐喻的具身认知描述一致。特别是,我们发现与更多抽象词相对应的图像在色彩空间中表现出更高的可变性,并且语义相似的词具有更相似的颜色分布。引人注目的是,我们表明颜色关联通常反映抽象领域之间共享的情感维度,从而揭示日常语言中审美连贯性的模式。我们认为,这些发现提供了一种新的方法来综合基于隐喻和基于情感的体现语义的解释。与基于隐喻的具身认知描述一致。特别是,我们发现与更多抽象词相对应的图像在色彩空间中表现出更高的可变性,并且语义相似的词具有更相似的颜色分布。引人注目的是,我们表明颜色关联通常反映抽象领域之间共享的情感维度,从而揭示日常语言中审美连贯性的模式。我们认为,这些发现提供了一种新的方法来综合基于隐喻和基于情感的体现语义的解释。从而揭示日常语言中审美连贯性的模式。我们认为,这些发现提供了一种新的方法来综合基于隐喻和基于情感的体现语义的解释。从而揭示日常语言中审美连贯性的模式。我们认为,这些发现提供了一种新的方法来综合基于隐喻和基于情感的体现语义的解释。
更新日期:2020-06-02
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