Clustering, hierarchical organization, and the topography of abstract and concrete nouns

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.

Keywords: abstract concepts; concreteness; embodied cognition; emotion; magnitude; semantic memory.

Figure 1

Scatterplots of mean Likert scale ratings (1–7; y-axis) for each of the 12 rating dimensions for words from across the concreteness spectrum (x-axis).

Figure 2

Three Dimensional Scatterplot Representing Abstract and Concrete Word Meaning. This view represents rotation about the axes/planes defined by the factors: Sens, sensation; Mag, magnitude; and Emo, emotion.

Figure 3

Dendrogram of hierarchical cluster analysis. Each cluster has been given a cluster number (e.g., C1,C2). The words inside each cluster can be found in Supplementary Material.