Large language models (LLMs) are demonstrating remarkable capabilities across various tasks despite lacking a foundation in human cognition. This raises the question: can these models, beyond simply mimicking human language patterns, offer insights into the mechanisms underlying human cognition? This study explores the ability of ChatGPT to predict human performance in a language-based memory task. Building upon theories of text comprehension, we hypothesize that recognizing ambiguous sentences (e.g., "Because Bill drinks wine is never kept in the house") is facilitated by preceding them with contextually relevant information. Participants, both human and ChatGPT, were presented with pairs of sentences. The second sentence was always a garden-path sentence designed to be inherently ambiguous, while the first sentence either provided a fitting (e.g., "Bill has chronic alcoholism") or an unfitting context (e.g., "Bill likes to play golf"). We measured both human's and ChatGPT's ratings of sentence relatedness, ChatGPT's memorability ratings for the garden-path sentences, and humans' spontaneous memory for the garden-path sentences. The results revealed a striking alignment between ChatGPT's assessments and human performance. Sentences deemed more related and assessed as being more memorable by ChatGPT were indeed better remembered by humans, even though ChatGPT's internal mechanisms likely differ significantly from human cognition. This finding, which was confirmed with a robustness check employing synonyms, underscores the potential of generative AI models to predict human performance accurately. We discuss the broader implications of these findings for leveraging LLMs in the development of psychological theories and for gaining a deeper understanding of human cognition.

通过测试ChatGPT在语言记忆任务中对人类表现的预测能力，研究发现ChatGPT和人类的表现有惊人的一致性，尽管它们的内部机制可能存在显著差异，这一发现强调了生成型人工智能模型在准确预测人类表现方面的潜力。

机器心理学：大型语言模型预测人类记忆