Words that make SENSE: Sensorimotor Norms in Learned Lexical Token Representations

arXiv:2602.00469v2 Announce Type: replace-cross
Abstract: While word embeddings derive meaning from co-occurrence patterns, human language understanding is grounded in sensory and motor experience. We present $textSENSE$ $(textbfStextensorimotor $ $textbfEtextmbedding $ $textbfNtextorm $ $textbfStextcoring $ $textbfEtextngine)$, a learned projection model that predicts Lancaster sensorimotor norms from word lexical embeddings. We also conducted a behavioral study where 281 participants selected which among candidate nonce words evoked specific sensorimotor associations, finding statistically significant correlations between human selection rates and $textSENSE$ ratings across 6 of the 11 modalities. Sublexical analysis of these nonce words selection rates revealed systematic phonosthemic patterns for the interoceptive norm, suggesting a path towards computationally proposing candidate phonosthemes from text data.