Large language models as linguistic simulators and cognitive models in human research

The rise of large language models (LLMs) that generate human-like text has sparked debates over their potential to replace human participants in behavioral and cognitive research. We critically evaluate this replacement perspective to appraise the fundamental utility of language models in psychology and social science. Through a five-dimension framework, characterization, representation, interpretation, implication, and utility, we identify six fallacies that undermine the replacement perspective: (1) equating token prediction with human intelligence, (2) assuming LLMs represent the average human, (3) interpreting alignment as explanation, (4) anthropomorphizing AI, (5) essentializing identities, and (6) purporting LLMs as primary tools that directly reveal the human mind. Rather than replacement, the evidence and arguments are consistent with a simulation perspective, where LLMs offer a new paradigm to simulate roles and model cognitive processes. We highlight limitations and considerations about internal, external, construct, and statistical validity, providing methodological guidelines for effective integration of LLMs into psychological research, with a focus on model selection, prompt design, interpretation, and ethical considerations. This perspective reframes the role of language models in behavioral and cognitive science, serving as linguistic simulators and cognitive models that shed light on the similarities and differences between machine intelligence and human cognition and thoughts.