arXiv:2605.22616v1 Announce Type: new 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 sensori
The increasing focus on embodied AI and questions of conceptual grounding drives the need for large-scale linguistic resources, especially for non-Indo-European languages where data is scarce.
This resource advances the understanding and development of AI systems capable of acquiring conceptual knowledge, potentially accelerating progress in embodied AI and cross-cultural understanding.
The availability of sensorimotor and embodiment norms for Chinese directly supports research into how conceptual knowledge is grounded across linguistic and cultural contexts, which was previously limited by data scarcity.
- · AI researchers
- · Cognitive scientists
- · Chinese tech sector
- · NLP researchers
- · AI models without diverse linguistic grounding
- · Monolingual AI research approaches
This dataset directly facilitates empirical investigations into embodied cognition and AI's ability to ground knowledge without direct experience.
Improved conceptual understanding in Chinese AI models could lead to more nuanced and culturally relevant AI applications.
The methodology could inspire similar data collection efforts for other underrepresented languages, fostering more globally inclusive AI development.
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Read at arXiv cs.CL