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This study describes an investigation of different types of semantic relationship among abstract words: antonyms (e.g. good-bad), synonyms (e.g. good-great), non-antonymous, non-synonymous associates (NANSAs; e.g. good-fun) and unrelated words (e.g. good-late). The comprehension and semantic properties of these words were examined using two distinct methodologies. Experiment 1 tested the comprehension of pairs of abstract words in three patients with global aphasia using a spoken word to written word matching paradigm. Contrary to expectations, all three patients showed superior antonym comprehension compared with synonyms or NANSAs, discriminating antonyms with a similar level of accuracy as unrelated words. Experiment 2 aimed to explore the content or semantic attributes of the abstract words used in Experiment 1 through the generation of control ratings across nine cognitive dimensions (sensation, action, thought, emotion, social interaction, space, time, quantity and polarity). Discrepancy analyses revealed that antonyms were as or more similar to one another than synonyms on all but one measure: polarity. The results of Experiment 2 provide a possible explanation for the novel pattern of neuropsychological data observed in Experiment 1, namely that polarity information is more important than other semantic attributes when discriminating the meaning of abstract words. It is argued that polarity is a critical semantic attribute of abstract words, and that simple 'dissimilarity' metrics mask fundamental consistencies in the semantic representation of antonyms. It is also suggested that mapping abstract semantic space requires the identification and quantification of the contribution made to abstract concepts by not only sensorimotor and emotional information but also a host of other cognitive dimensions.

Original publication




Journal article



Publication Date





2636 - 2644


Aged, Aphasia, Association Learning, Concept Formation, Female, Humans, Knowledge, Male, Middle Aged, Neuropsychological Tests, Semantics, Statistics, Nonparametric, Stroke, Vocabulary