Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives
Year: 2026
Type: conference-paper
Abstract: Do large language models (LLMs) truly acquire embodied cognition and cultural conventions from text?We introduce demonstratives, fundamental spatial expressions like "this/that" in English and "这/那" in Chinese, as a novel probe for grounded knowledge.Using 6,400 responses from 320 native speakers, we establish a human baseline: English speakers reliably distinguish proximal-distal referents but struggle with perspective-taking, while Chinese speakers switch perspectives fluently but tolerate distal ambiguity.In contrast, five state-ofthe-art LLMs fail to inherently understand the proximal-distal contrast and show no cultural differences, defaulting to English-centric reasoning.Our study contributes (i) a new task, based on demonstratives, as a new lens for evaluating embodied cognition and cultural conventions; (ii) empirical evidence of cross-cultural asymmetries in human interpretation; (iii) a new perspective on the egocentric-sociocentric debate, showing both orientations coexist but vary across languages; and (iv) a call to address individual variation in future model design.Model Gpt5.
Authors: Yu Wang, Emmanuele Chersoni, Chu-Ren Huang
Institution: Hong Kong Polytechnic University
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Funder: Hong Kong Polytechnic University
OpenAlex ID: W7166874823