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        <title>Investigating Rich Feature Sources for Conceptual Representation Encoding</title>
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        <namePart type="given">Lu</namePart>
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    <name type="personal">
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            <title>Proceedings of the Workshop on the Cognitive Aspects of the Lexicon</title>
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            <namePart type="given">Michael</namePart>
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    <abstract>Functional Magnetic Resonance Imaging (fMRI) provides a means to investigate human conceptual representation in cognitive and neuroscience studies, where researchers predict the fMRI activations with elicited stimuli inputs. Previous work mainly uses a single source of features, particularly linguistic features, to predict fMRI activations. However, relatively little work has been done on investigating rich-source features for conceptual representation. In this paper, we systematically compare the linguistic, visual as well as auditory input features in conceptual representation, and further introduce associative conceptual features, which are obtained from Small World of Words game, to predict fMRI activations. Our experimental results show that those rich-source features can enhance performance in predicting the fMRI activations. Our analysis indicates that information from rich sources is present in the conceptual representation of human brains. In particular, the visual feature weights the most on conceptual representation, which is consistent with the recent cognitive science study.</abstract>
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%0 Conference Proceedings
%T Investigating Rich Feature Sources for Conceptual Representation Encoding
%A Cao, Lu
%A Chen, Yulong
%A Huang, Dandan
%A Zhang, Yue
%Y Zock, Michael
%Y Chersoni, Emmanuele
%Y Lenci, Alessandro
%Y Santus, Enrico
%S Proceedings of the Workshop on the Cognitive Aspects of the Lexicon
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F cao-etal-2020-investigating
%X Functional Magnetic Resonance Imaging (fMRI) provides a means to investigate human conceptual representation in cognitive and neuroscience studies, where researchers predict the fMRI activations with elicited stimuli inputs. Previous work mainly uses a single source of features, particularly linguistic features, to predict fMRI activations. However, relatively little work has been done on investigating rich-source features for conceptual representation. In this paper, we systematically compare the linguistic, visual as well as auditory input features in conceptual representation, and further introduce associative conceptual features, which are obtained from Small World of Words game, to predict fMRI activations. Our experimental results show that those rich-source features can enhance performance in predicting the fMRI activations. Our analysis indicates that information from rich sources is present in the conceptual representation of human brains. In particular, the visual feature weights the most on conceptual representation, which is consistent with the recent cognitive science study.
%U https://aclanthology.org/2020.cogalex-1.2
%P 12-22

Markdown (Informal)

[Investigating Rich Feature Sources for Conceptual Representation Encoding](https://aclanthology.org/2020.cogalex-1.2) (Cao et al., CogALex 2020)

• Investigating Rich Feature Sources for Conceptual Representation Encoding (Cao et al., CogALex 2020)

ACL

• Lu Cao, Yulong Chen, Dandan Huang, and Yue Zhang. 2020. Investigating Rich Feature Sources for Conceptual Representation Encoding. In Proceedings of the Workshop on the Cognitive Aspects of the Lexicon, pages 12–22, Online. Association for Computational Linguistics.