Predicting Human Similarity Judgments Using Large Language Models

Understand

Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning.

• However, collecting similarity judgments can be prohibitively expensive for naturalistic datasets as the number of comparisons grows quadratically in the number of stimuli.
• One way to tackle this problem is to construct approximation procedures that rely on more accessible proxies for predicting similarity.
• Here we leverage recent advances in language models and online recruitment, proposing an efficient domain-general procedure for predicting human similarity judgments based on text descriptions.