The nature of the neural code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. Three main types of information have been proposed as candidates for the neural representations of lexical concepts: taxonomic (i.e., information about category membership and inter-category relations), distributional (i.e., information about patterns of word co-occurrence in natural language use), and experiential (i.e., information about sensory-motor, affective, and other features of phenomenal experience engaged during concept acquisition). In two experiments, we investigated the extent to which these three types of information are encoded in the neural activation patterns associated with hundreds of English nouns from a wide variety of conceptual categories. Participants made familiarity judgments on the meaning of written nouns while undergoing functional MRI. A high-resolution, whole-brain activation map was generated for each noun in each participant′s native space. These word-specific activation maps were used to evaluate different representational spaces corresponding to the three types of information described above. In both studies, we found a striking advantage for experience-based models in most brain areas previously associated with concept representation. Partial correlation analyses revealed that only experiential information successfully predicted concept similarity structure when inter-model correlations were taken into account. This pattern of results was found independently for object concepts and event concepts. Our findings indicate that the neural representation of conceptual knowledge primarily encodes information about features of experience, and that - to the extent that it is represented in the brain - taxonomic and distributional information may rely on such an experience-based code.
Functional MRI Neural Activation Patterns in Early and Late Talkers
