  To what extent can language alone give rise to complex concepts, or is
embodied experience essential? Recent advancements in large language models
(LLMs) offer fresh perspectives on this question. Although LLMs are trained on
restricted modalities, they exhibit human-like performance in diverse
psychological tasks. Our study compared representations of 4,442 lexical
concepts between humans and ChatGPTs (GPT-3.5 and GPT-4) across multiple
dimensions, including five key domains: emotion, salience, mental
visualization, sensory, and motor experience. We identify two main findings: 1)
Both models strongly align with human representations in non-sensorimotor
domains but lag in sensory and motor areas, with GPT-4 outperforming GPT-3.5;
2) GPT-4's gains are associated with its additional visual learning, which also
appears to benefit related dimensions like haptics and imageability. These
results highlight the limitations of language in isolation, and that the
integration of diverse modalities of inputs leads to a more human-like
conceptual representation.
