The Mind in the Machine: A Survey of Incorporating Psychological Theories in LLMs

Psychological insights have long shaped pivotal NLP breakthroughs, including the cognitive underpinnings of attention mechanisms, formative reinforcement learning, and Theory of Mind-inspired social modeling. As Large Language Models (LLMs) continue to grow in scale and complexity, there is a rising consensus that psychology is essential for capturing human-like cognition, behavior, and interaction. This paper reviews how psychological theories can inform and enhance stages of LLM development, including data, pre-training, post-training, and evaluation\&application. Our survey integrates insights from cognitive, developmental, behavioral, social, personality psychology, and psycholinguistics. Our analysis highlights current trends and gaps in how psychological theories are applied. By examining both cross-domain connections and points of tension, we aim to bridge disciplinary divides and promote more thoughtful integration of psychology into future NLP research.

@article{liu2025_2505.00003,
  title={ The Mind in the Machine: A Survey of Incorporating Psychological Theories in LLMs },
  author={ Zizhou Liu and Ziwei Gong and Lin Ai and Zheng Hui and Run Chen and Colin Wayne Leach and Michelle R. Greene and Julia Hirschberg },
  journal={arXiv preprint arXiv:2505.00003},
  year={ 2025 }
}