The Primacy of Experience in Language Processing: Semantic Priming Is Driven Primarily by Experiential Similarity

Abstract

The organization of the representational space underlying the neural coding of word meaning has long been a central question in cognitive science. Although there is general agreement that lexical semantic representations must make contact with sensory-motor and affective experiences in a non-arbitrary fashion, the nature of this relationship remains controversial. Many researchers have proposed that lexical concepts (i.e., word meanings) are represented primarily in terms of their experiential content, ultimately derived from sensory-motor and affective processes. However, the recent success of distributional language models in emulating human linguistic behavior has led to proposals that word co-occurrence information may play a more important role in the representation of concepts in the human brain. We investigated this issue by using representational similarity analysis (RSA) of semantic priming data. Participants performed a speeded lexical decision task in two sessions separated by approximately one week. All target words were presented once in each session, but each time they were preceded by a different prime word. Priming was computed for each target as the difference in RT between the two sessions. We evaluated eight models of semantic word representation in terms of their ability to predict the magnitude of the priming effect for each target: two based on experiential information, three based on distributional information, and three based on taxonomic information. Crucially, we used partial correlation RSA to account for intercorrelations between predictions from different models, which allowed us to assess, for the first time, the unique predictive power of experiential and distributional models. We found that semantic priming was driven primarily by experiential similarity between prime and target, with no evidence of an independent effect of distributional similarity. These results support experiential accounts of language representation and indicate that, despite their good performance at some linguistic tasks, distributional models do not encode the same kind of information used by the brain.