Semantic Representations Extracted from Large Language Corpora Predict High-level Human Judgment in Seven Diverse Behavioral Domains

Recent advances in machine learning, combined with the increased availability of large natural language datasets, have made it possible to uncover semantic representations that characterize what people know about and associate with a wide range of objects and concepts. In this paper, we examine the power of word embeddings, a popular approach for uncovering semantic representations, for studying high-level human judgment. Word embeddings are typically applied to linguistic and semantic tasks, however we show that word embeddings can be used to predict complex theoretically -and practically- relevant human perceptions and evaluations in domains as diverse as social cognition, health behavior, risk perception, organizational behavior, and marketing. By learning mappings from word embeddings directly onto judgment ratings, we outperform a similarity-based baseline as well as common metrics of human inter-rater reliability. Word embeddings are also able to identify the concepts that are most associated with observed perceptions and evaluations, and can thus shed light on the psychological substrates of judgment. Overall, we provide new methods for predicting and understanding high-level human judgment, with important applications across the social and behavioral sciences.

Location:
3401 Walnut Street, Room 401B of the Warren Center for Network and Data Sciences

Time:
1:30pm, Snacks provided!