Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety. Here we introduce Centaur, a computational model that can predict and simulate human behavior in any experiment expressible in natural language. We derived Centaur by finetuning a state-of-the-art language model on a novel, large-scale data set called Psych-101. Psych-101 reaches an unprecedented scale, covering trial-by-trial data from over 60,000 participants performing over 10,000,000 choices in 160 experiments. Centaur not only captures the behavior of held-out participants better than existing cognitive models, but also generalizes to new cover stories, structural task modifications, and entirely new domains. Furthermore, we find that the model's internal representations become more aligned with human neural activity after finetuning. Taken together, Centaur is the first real candidate for a unified model of human cognition. We anticipate that it will have a disruptive impact on the cognitive sciences, challenging the existing paradigm for developing computational models.

翻译：建立统一的认知理论一直是心理学的主要目标。尽管先前已有通过构建计算模型来实例化此类理论的尝试，但目前我们尚未拥有一个能够完整捕捉人类心智的模型。本文介绍Centaur，一种能够预测和模拟任何可用自然语言表述的实验中人行为的计算模型。我们通过对最先进的语言模型在一个名为Psych-101的新型大规模数据集上进行微调，从而推导出Centaur。Psych-101达到了前所未有的规模，涵盖了来自超过60,000名参与者在160个实验中完成超过10,000,000次选择的逐试次数据。Centaur不仅比现有认知模型更好地捕捉了预留参与者的行为，还能泛化至新的背景故事、结构性任务修改以及全新的领域。此外，我们发现模型的内在表征在微调后与人类神经活动更加对齐。综上所述，Centaur是首个真正有望成为人类认知统一模型的候选者。我们预期它将对认知科学产生颠覆性影响，挑战现有开发计算模型的范式。