Automating psychological hypothesis generation with AI: when large language models meet causal graph

Song Tong, Kai Mao, Zhen Huang, Yukun Zhao () and Kaiping Peng ()
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Song Tong: Tsinghua University
Kai Mao: Kindom KK
Zhen Huang: Tsinghua University
Yukun Zhao: Tsinghua University
Kaiping Peng: Tsinghua University

Palgrave Communications, 2024, vol. 11, issue 1, 1-14

Abstract: Abstract Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link prediction algorithms, we generated 130 potential psychological hypotheses focusing on “well-being”, then compared them against research ideas conceived by doctoral scholars and those produced solely by the LLM. Interestingly, our combined approach of a LLM and causal graphs mirrored the expert-level insights in terms of novelty, clearly surpassing the LLM-only hypotheses (t(59) = 3.34, p = 0.007 and t(59) = 4.32, p

Date: 2024
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DOI: 10.1057/s41599-024-03407-5

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