The new study by Xu et al. uses large language models as an experimental tool to probe those influences. Treating LLMs as a controlled source of purely linguistic experience allows researchers to compare model-derived semantic patterns with human behavior and brain responses. That comparison helps separate patterns that arise from language alone from those that require sensorimotor grounding.
Understanding which parts of meaning come from words and which come from bodily experience changes how we support people with different abilities and learning histories. The work invites deeper questions: how might education leverage language to bootstrap sensorimotor concepts, and how could assistive systems combine linguistic and sensory inputs to better reflect human thinking? Follow the full article to see how these methods map onto human potential, learning, and inclusion.
A long-standing question in cognitive sciences concerns the specific contribution of linguistic and sensorimotor experience in shaping conceptual knowledge. A new study by Xu et al. shows that large language models (LLMs) represent a powerful tool to advance this debate, helping to disentangle the relative contribution of different experiential modalities.
