Neural Representations of Abstract Concepts: Identifying Underlying Neurosemantic Dimensions

Abstract

The abstractness of concepts is sometimes defined indirectly as lacking concreteness, this view provides little insight into their cognitive or neural basis. Multivariate pattern analytic techniques applied to functional magnetic resonance imaging data were used to characterize the neural representations of 28 individual abstract concepts. A classifier trained on the concepts' neural signatures reliably decoded their neural representations in an independent subset of data for each participant. There was considerable commonality of the neural representations across participants as indicated by the accurate classification of each participant's concepts based on the neural signatures obtained in other participants. Group-level factor analysis revealed 3 semantic dimensions underlying the 28 concepts, suggesting a brain-based ontology for this set of abstract concepts. The 3 dimensions corresponded to 1) the degree a concept was Verbally Represented; 2) whether a concept was External (or Internal) to the individual, and 3) whether the concept contained Social Content. Further exploration of the Verbal Representation dimension suggests that the degree a concept is verbally represented can be construed as a point on a continuum between language faculties and perceptual faculties. A predictive model, based on independent behavioral ratings of the 28 concepts along the 3 factor dimensions, provided converging evidence for the interpretations.

Keywords: MVPA; abstract concepts; fMRI; predictive modeling; semantic.