Chinese sensorimotor and embodiment norms for 3,000 lexicalized concepts

2026-05-21

AI Summary

This study addresses the scarcity of large-scale, high-dimensional sensorimotor and embodiment norm resources for non-Indo-European languages like Chinese, which has hindered investigations into the bodily grounding of conceptual knowledge. Leveraging ratings from 378 native speakers on 3,000 Chinese words across 11 sensorimotor dimensions and one embodiment dimension, the authors construct the first comprehensive Chinese sensorimotor norm database and validate its efficacy through lexical decision tasks and language representation recovery experiments. The work introduces a novel metric, PSE-Sensorimotor, which significantly predicts word processing performance—optimally under the Minkowski-3 model. It further demonstrates that language models can partially reconstruct sensorimotor structure (mean Spearman’s ρ = 0.62), with visual and auditory dimensions more accurately recovered than chemosensory ones, and successfully recovers the geometric structure of the sensorimotor space from linguistic representations (ρ = 0.540).

Abstract

Understanding how conceptual knowledge is grounded in bodily experience, and to what extent machine systems can acquire such knowledge without direct sensorimotor experience, are central questions in both cognitive science and embodied artificial intelligence research. Large-scale normative resources are essential for investigating these questions empirically, yet such resources remain sparse for non-Indo-European languages. We present a novel normative database for 3,000 lexicalized concepts in Mandarin Chinese, comprising 11-dimensional sensorimotor ratings and unidimensional embodiment ratings collected from 378 native Mandarin speakers. The ratings demonstrate high reliability and strong cross-norm validity with existing Chinese resources, each of which covers fewer words and a subset of the 11 sensorimotor dimensions. In a validation study, we tested new variables derived from a theoretically motivated metric, Perceptual Strength of Embodiment (PSE) (Huang et al., 2025), together with seven common composite variables, on lexical decision tasks. The results suggest that PSE-Sensorimotor and Minkowski-3 are the strongest composite predictors of lexical decision performance, capturing the facilitatory effects of sensorimotor information on lexical processing. A further exploratory study showed that sensorimotor ratings are substantially recoverable from purely linguistic representations using simple regression models (mean Spearman r = .62 across dimensions), though recovery varied markedly: visual and auditory dimensions yielded higher correspondence than chemosensory ones. Representational similarity analysis further showed that the relational geometry of the sensorimotor space is also partially recoverable (r = .540), consistent with the view that distributional language use encodes aspects of embodied conceptual structure.

Problem

• sensorimotor norms
• embodiment
• lexical concepts
• Chinese language
• grounded cognition

Innovation

• sensorimotor norms
• embodied cognition
• lexical decision
• distributional semantics
• Perceptual Strength of Embodiment