Large language models (LLMs) like ChatGPT and DeepSeek offer transformative potential for psychology—from research tools to clinical aids—but require rigorous scrutiny to address biases, ethical pitfalls, and the fundamental limits of non-conscious systems. This Methods Collection explores the intersection of psychology and LLMs, examining how these AI systems simulate human cognition, influence user behavior, and reshape communication. 

The rise of LLMs has transformed human-computer interaction, influencing communication, decision-making, and cognition. As AI systems become more integrated into daily life—assisting in education, mental health, and social interactions—it is crucial to understand their psychological impact. Key concerns include trust, bias, emotional influence, and cognitive processing, making this an urgent area for interdisciplinary research. 

This Methods Collection aims to provide rigorous methodologies for studying LLMs from a psychological perspective. It will help researchers in the following ways: 

• Develop empirical frameworks to analyze how humans interact with LLMs. 
• Establish standardized methods for assessing cognitive biases, emotional responses, and ethical concerns. 
• Enhance experimental designs for testing trust, perception, and persuasion in AI-human communication. 
• Bridge psychology, linguistics, and AI research, fostering interdisciplinary collaboration.