Abstract: With the rapid development of Large Language Models (LLMs), their human-like outputs are increasingly interpreted through psychological frameworks, which has provoked intense debate about whether LLMs can serve as objects of psychological inquiry. We argue that these debates are not merely methodological, but reflect deeper divergences in interpretive stances toward LLMs' human-like outputs. Building on the distinction between Physical, Design, and Intentional stance, this study develops an integrative theoretical framework that links these practical stances to different levels of interpreting LLMs’ human-like outputs. Within this framework, we identify three major forms of psychologization: instrumental, functional, and agent-level, and systematically examine their practical approaches and limitations. We further analyze the potential pitfalls associated with these practices, particularly those arising from bias, methodological instability, and anthropomorphism. In light of these challenges, we outline future research directions, highlighting the potential of predictive processing theory in cognitive modeling and developing a new paradigm of intelligent software based on LLM-driven mind modules for proactive interaction.

Key words: large language models, psychologization, intentional stance, artificial theory of mind, theoretical framework