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.
@article{arxiv.2402.03618,
title = {Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction},
author = {Sreejan Kumar and Raja Marjieh and Byron Zhang and Declan Campbell and Michael Y. Hu and Umang Bhatt and Brenden Lake and Thomas L. Griffiths},
journal= {arXiv preprint arXiv:2402.03618},
year = {2024}
}