Refining the multimodality of semantic representations.

Area of Science:

• Cognitive Science
• Computational Linguistics
• Neuroscience

Background:

• The origin of conceptual knowledge is debated, with theories emphasizing either linguistic input or sensorimotor experiences.
• Disentangling these influences has been challenging due to the complex interplay of sensory and linguistic data in human development.

Purpose of the Study:

• To investigate the distinct roles of linguistic and sensorimotor experience in the formation of conceptual knowledge.
• To leverage large language models (LLMs) as a novel tool to model and analyze these influences.

Main Methods:

• Utilized advanced large language models (LLMs) to process and represent vast amounts of text data.
• Designed experiments to compare conceptual representations derived from purely linguistic data versus those incorporating simulated sensorimotor information.
• Analyzed the differences in conceptual representations generated by the LLMs under varying input conditions.

Main Results:

• LLMs demonstrated the ability to capture nuanced aspects of conceptual knowledge.
• Results indicated that linguistic experience significantly contributes to abstract conceptual understanding.
• Sensorimotor experience appears crucial for grounding concepts in a more embodied manner.

Conclusions:

• Large language models (LLMs) offer a powerful computational approach to address long-standing questions in cognitive science.
• The study provides evidence for the differential contributions of linguistic and sensorimotor modalities in shaping conceptual knowledge.
• Future research can further refine LLM architectures to better model human conceptual development.