Automating Psychological Hypothesis Generation with AI: 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 the LLM and other machine learning tools, extracting 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 LLM and causal graph mirrored expert-level insights in terms of novelty, clearly surpassing the LLM-only hypotheses. This alignment was further corroborated using deep semantic analysis. Our results show that combining LLM with machine learning techniques like causal knowledge graphs can revolutionize automated discovery in psychology, extracting novel insights from 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.