Cross-Modal Causal Inference Framework for Integrating Language, Physiology, and Environment in Sports Performance Modeling

Abstract

In this study, we constructed a cross-modal causal inference framework (CMCI) for language-actionperformance (LAP), integrating linguistic, physiological and environmental multimodal data, to reveal the causal mechanism of linguistic cognition on motor performance and cross-cultural differences. Spatiotemporal alignment of the multimodal data (error mean <50ms) was achieved by Dynamic Temporal Warping (DTW) algorithm, which was combined with Constrained Neurocausal Modelling (CNCM) to inject a priori knowledge of the domain (e.g., verbal anxiety reduced HRV reduced movement stability). Through long-term data from the Premier League’s youth training (N=120) and cross-cultural samples (East Asia n=2,150 / North America n=1,920 / Europe n=3,780), we demonstrated an inverted U-shaped relationship between language complexity (MLU index) and motor control accuracy (optimal MLU = 4.2, p = 0.012). Collectivist language reduced team cooperation errors among East Asian athletes by 22% (β = 0.41, p < 0.01). The model’s prediction accuracy for injury risk (AUC) reached 0.89, and the mean absolute error (MAE) for causal effect estimation was only 0.07. The study open-sources the M-SPORT 2.0 multimodal corpus and SportNLP-Causality toolkit to provide a reproducible interdisciplinary research paradigm for sports science, and to promote the innovation of culturally appropriate theories and practices for globalised sports training.