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：本研究利用因果知识图与大语言模型的协同作用，提出了一种计算化心理学假设生成的突破性方法。我们使用大语言模型分析43,312篇心理学文章，从中提取因果关系对，构建了专用于心理学的因果图。通过应用链接预测算法，我们生成了130个聚焦于"幸福感"的心理学潜在假设，并将其与博士生构思的研究思路及单独由大语言模型生成的假设进行对比。研究发现，大语言模型与因果图的组合方法在新颖性方面达到了专家级别的洞察水平，明显优于单独使用大语言模型的假设（t(59)=3.34，p=0.007；t(59)=4.32，p<0.001）。通过深层语义分析进一步验证了这一一致性。结果表明，将大语言模型与因果知识图等机器学习技术相结合，能够从海量文献中提取新颖见解，彻底革新心理学自动发现过程。该研究处于心理学与人工智能的交叉领域，为心理学研究中的数据驱动假设生成开创了一种新的丰富范式。