Comparing Abstraction in Humans and Large Language Models Using
  Multimodal Serial Reproduction
Sreejan Kumar Raja Marjieh Byron Zhang Declan Campbell Michael Y. Hu Umang Bhatt Brenden Lake Thomas L. Griffiths
Humans extract useful abstractions of the world from noisy sensory data.
Serial reproduction allows us to study how people construe the world through a
paradigm similar to the game of telephone, where one person observes a stimulus
and reproduces it for the next to form a chain of reproductions. Past serial
reproduction experiments typically employ a single sensory modality, but humans
often communicate abstractions of the world to each other through language. To
investigate the effect language on the formation of abstractions, we implement
a novel multimodal serial reproduction framework by asking people who receive a
visual stimulus to reproduce it in a linguistic format, and vice versa. We ran
unimodal and multimodal chains with both humans and GPT-4 and find that adding
language as a modality has a larger effect on human reproductions than GPT-4's.
This suggests human visual and linguistic representations are more dissociable
than those of GPT-4.