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谱与大语言模型（LLM）的协同作用，提出了一种突破性的心理学计算假设生成方法。我们使用LLM分析了43,312篇心理学文献以提取因果关系对，由此构建了一个心理学专用因果图谱。通过应用链路预测算法，我们生成了130个聚焦“幸福感”的潜在心理学假设，并将其与博士学者构思的研究思路及纯LLM生成的假设进行了比较。有趣的是，我们的LLM与因果图谱结合方法在创新性方面呈现出与专家级见解相当的匹配度，明显超越了纯LLM生成的假设（分别为t(59) = 3.34, p=0.007 和 t(59) = 4.32, p<0.001）。深度语义分析进一步证实了这种一致性。研究结果表明，将LLM与因果知识图谱等机器学习技术相结合，能够革新心理学的自动化发现过程，从海量文献中提取新颖见解。本工作立足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倡导了一种全新的增强范式。