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.

翻译：利用因果知识图谱与大型语言模型（LLM）的协同作用，本研究提出了一种心理学计算化假设生成的突破性方法。我们通过LLM分析了43,312篇心理学论文，提取因果关系对，构建了心理学专用因果图。运用链接预测算法，我们生成了130个聚焦于“幸福感”的潜在心理学假设，并将其与博士学者构思的研究想法及纯LLM生成的假设进行对比。值得注意的是，我们的LLM与因果图联合方法在创新性上达到了专家级水平，显著优于纯LLM生成的假设（t(59)=3.34, p=0.007; t(59)=4.32, p<0.001）。深度语义分析进一步验证了这一一致性。结果表明，将LLM与因果知识图谱等机器学习技术相结合，能从海量文献中提炼新颖见解，彻底革新心理学的自动化发现过程。本研究处在心理学与人工智能的交叉点，推动了心理学研究中数据驱动假设生成的新范式发展。