Automating psychological hypothesis generation with AI: when large language models meet causal graph.
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...
| Published in: | Humanities & Social Sciences Communications Vol. 11; no. 1; pp. 1 - 15 |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Published: |
Springer Nature
7/9/2024
|
| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=178354343&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 178354343 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: MVR0 jtl: Humanities & Social Sciences Communications maglogo: N pubinfo: dt: 7/9/2024 vid: 11 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 178354343 10.1057/s41599-024-03407-5 ppf: 1 ppct: 14 formats: tig: atl: Automating psychological hypothesis generation with AI: when large language models meet causal graph. aug: au: Tong, Song Mao, Kai Huang, Zhen Zhao, Yukun Peng, Kaiping affil: https://ror.org/03cve4549 Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China https://ror.org/03cve4549 Positive Psychology Research Center, School of Social Sciences, Tsinghua University, Beijing, China https://ror.org/03cve4549 AI for Wellbeing Lab, Tsinghua University, Beijing, China https://ror.org/03cve4549 Institute for Global Industry, Tsinghua University, Beijing, China Kindom KK, Tokyo, Japan su: Language models Causal models Knowledge graphs Artificial intelligence Machine learning Well-being sug: subj: Language models Causal models Knowledge graphs Artificial intelligence Machine learning Well-being ab: 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 < 0.001, respectively). This alignment was further corroborated using deep semantic analysis. Our results show that combining LLM with machine learning techniques such as causal knowledge graphs can revolutionize automated discovery in psychology, extracting novel insights from the extensive literature. This work stands at the crossroads of psychology and artificial intelligence, championing a new enriched paradigm for data-driven hypothesis generation in psychological research. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
|---|