Title |
Clustering, hierarchical organization, and the topography of abstract and concrete nouns
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Published in |
Frontiers in Psychology, April 2014
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DOI | 10.3389/fpsyg.2014.00360 |
Pubmed ID | |
Authors |
Joshua Troche, Sebastian Crutch, Jamie Reilly |
Abstract |
The empirical study of language has historically relied heavily upon concrete word stimuli. By definition, concrete words evoke salient perceptual associations that fit well within feature-based, sensorimotor models of word meaning. In contrast, many theorists argue that abstract words are "disembodied" in that their meaning is mediated through language. We investigated word meaning as distributed in multidimensional space using hierarchical cluster analysis. Participants (N = 365) rated target words (n = 400 English nouns) across 12 cognitive dimensions (e.g., polarity, ease of teaching, emotional valence). Factor reduction revealed three latent factors, corresponding roughly to perceptual salience, affective association, and magnitude. We plotted the original 400 words for the three latent factors. Abstract and concrete words showed overlap in their topography but also differentiated themselves in semantic space. This topographic approach to word meaning offers a unique perspective to word concreteness. |
X Demographics
Geographical breakdown
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United Kingdom | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Malaysia | 1 | <1% |
Sweden | 1 | <1% |
Unknown | 114 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 29 | 25% |
Researcher | 18 | 16% |
Student > Bachelor | 12 | 10% |
Student > Master | 9 | 8% |
Student > Doctoral Student | 6 | 5% |
Other | 20 | 17% |
Unknown | 22 | 19% |
Readers by discipline | Count | As % |
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Psychology | 30 | 26% |
Linguistics | 16 | 14% |
Neuroscience | 12 | 10% |
Agricultural and Biological Sciences | 5 | 4% |
Nursing and Health Professions | 3 | 3% |
Other | 20 | 17% |
Unknown | 30 | 26% |