Abstract Neuronal populations code similar concepts by similar activity patterns across the human brain’s networks supporting language comprehension. However, it is unclear to what extent such meaning-to-symbol mapping reflects statistical distributions of symbol meanings in language use, as quantified by word co-occurrence frequencies, or, rather, experiential information thought to be necessary for grounding symbols in sensorimotor knowledge. Here we asked whether integrating distributional semantics with human judgments of grounded sensorimotor semantics better approximates the representational similarity of conceptual categories in the brain, as compared with each of these methods used separately. We examined the similarity structure of activation patterns elicited by action- and object-related concepts using multivariate representational similarity analysis (RSA) of fMRI data. The results suggested that a semantic vector integrating both sensorimotor and distributional information yields best category discrimination on the cognitive-linguistic level, and explains the corresponding activation patterns in left posterior inferior temporal cortex. In turn, semantic vectors based on detailed visual and motor information uncovered category-specific similarity patterns in fusiform and angular gyrus for object-related concepts, and in motor cortex, left inferior frontal cortex (BA 44), and supramarginal gyrus for action-related concepts.

Category-specific representational patterns in left inferior frontal and temporal cortex reflect similarities and differences in the sensorimotor and distributional properties of concepts