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

arXiv: https://arxiv.org/abs/2602.00469 · HTML: https://mlsanity.com/paper/2602.00469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Builds on (most cited first, 1 of 1)

- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)

## Abstract (arXiv)

While word embeddings derive meaning from co-occurrence patterns, human language understanding is grounded in sensory and motor experience. We present SENSE (Sensorimotor  Embedding  Norm  Scoring  Engine), 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 SENSE 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.