大模型与心理认知融合实验: 现状，挑战与展望

The widespread social penetration of Large Language Models (LLMs) is reshaping human social landscapes, making the systematic study of psychological mechanisms in human-LLM co-evolution a frontier research area. This paper systematically analyzes the impact of LLM technology on psychological experiments through three distinct levels: cognitive mechanism comparison studies, human subject simulation experiments, and multi-agent human-machine interaction experiments. Cognitive Mechanism Analysis: Research reveals that LLMs exhibit human-like characteristics in perceptual judgment, reasoning, and decision-making tasks, achieving or surpassing human performance in many cognitive domains. However, fundamental differences exist between LLM and human cognitive mechanisms, particularly in memory and forgetting processes, causal reasoning, and theory of mind capabilities. While LLMs demonstrate perfect short-term memory retention and lack forgetting mechanisms, humans show complex memory dynamics. These differences necessitate careful consideration in experimental design and evaluation metrics. Human Subject Simulation: LLMs demonstrate remarkable ability to simulate fine-grained cognitive features, including cognitive dissonance, emotional responses, and social behaviors. However, significant limitations exist, including black-box properties, homogenization tendencies due to alignment techniques, and poor performance in simulating specific demographic characteristics. These constraints raise concerns about ecological validity when LLMs completely substitute human subjects in psychological experiments. Multi-Agent Human-Machine Interaction: LLMs show promise as novel social entities in various experimental paradigms, from one-on-one interactions to large-scale social simulations. In dyadic experiments, LLMs can simulate emotional states and engage in empathetic interactions, though challenges remain in balancing expressiveness with naturalness. In multi-agent scenarios, LLMs participate in game-theoretic settings like prisoner's dilemmas and public goods games, revealing complex strategic capabilities but limitations in theory of mind reasoning. Large-scale social simulations using thousands of LLM agents provide unprecedented opportunities to study collective behavior and social dynamics. Experimental Framework and Platforms: The paper outlines a standardized workflow for LLM-integrated psychological experiments comprising 12 core tasks across four phases: proposal, preparation, execution, and data analysis. The complexity of human-machine interaction experiments demands advanced tools and specialized platforms. The emerging experiment platform addresses these challenges through native LLM integration, visual design systems, and multi-agent simulation capabilities, though limitations exist in physiological measurement support. Future Directions: The rapid iteration of LLM technology and technical complexity of human-machine experimental deployment present ongoing challenges. Future research requires developing LLM-native experimental frameworks, modular visualization systems, and comprehensive platforms supporting diverse experimental paradigms. As AI agents become more autonomous and sophisticated, new psychological questions regarding ethics, safety, and human-machine relationships will emerge, necessitating innovative experimental approaches grounded in psychological theory. This comprehensive review highlights both the transformative potential and inherent limitations of LLM integration in psychological research, providing essential insights for researchers navigating this rapidly evolving interdisciplinary landscape.