ArticleProceedings of the National Academy of Sciences of the United States of America2023
Predicting implicit attitudes with natural language data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Snake Oil or Panacea? How to Misuse AI in Scientific Inquiries of the Human Mind.Behavioral sciences (Basel, Switzerland) · 2026Review
- Large language model-based evaluation of the impact of gender in medical research.medRxiv : the preprint server for health sciences · 2026Article
- Evolution and Observable Properties of Rocky Planet Atmospheres.Space science reviews · 2026Review
- Happier individuals generate more spontaneous thoughts about friends and value relationships over money.Communications psychology · 2025Article
- Computational analysis of 100 K choice dilemmas: Decision attributes, trade-off structures, and model-based prediction.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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2 authors.
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Abstract
Large-scale language datasets and advances in natural language processing offer opportunities for studying people's cognitions and behaviors. We show how representations derived from language can be combined with laboratory-based word norms to predict implicit attitudes for diverse concepts. Our approach achieves substantially higher correlations than existing methods. We also show that our approach is more predictive of implicit attitudes than are explicit attitudes, and that it captures variance in implicit attitudes that is largely unexplained by explicit attitudes. Overall, our results shed light on how implicit attitudes can be measured by combining standard psychological data with large-scale language data. In doing so, we pave the way for highly accurate computational modeling of what people think and feel about the world around them.
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