Large language models are powerful systems that excel at many tasks, ranging
from translation to mathematical reasoning. Yet, at the same time, these models
often show unhuman-like characteristics. In the present paper, we address this
gap and ask whether large language models can be turned into cognitive models.
We find that -- after finetuning them on data from psychological experiments --
these models offer accurate representations of human behavior, even
outperforming traditional cognitive models in two decision-making domains. In
addition, we show that their representations contain the information necessary
to model behavior on the level of individual subjects. Finally, we demonstrate
that finetuning on multiple tasks enables large language models to predict
human behavior in a previously unseen task. Taken together, these results
suggest that large, pre-trained models can be adapted to become generalist
cognitive models, thereby opening up new research directions that could
transform cognitive psychology and the behavioral sciences as a whole.

本研究旨在探讨大型语言模型是否能够成为认知模型，并发现通过在心理实验数据上微调这些模型，它们能够提供准确的人类行为表示，甚至在两个决策制定领域中表现比传统的认知模型更好。此外，我们展示了它们的表示含有模拟个体受试者行为所需的信息，并演示了在多个任务上进行微调的能力使得大型模型能够预测以前从未见过的任务中人类行为，这表明预先训练的大型模型可以被改进为通用认知模型，从而开辟了颠覆认知心理学和行为科学的新研究方向。