用自动化方法优化认知模型，让机器更懂人类决策
Automated scientific minimization of regret
- 基于人类认知基础模型，自动识别认知模型的缺陷
- 在多属性决策任务中，预测效果逼近人类表现上限
- 适合研究认知科学与人工智能融合的学者使用
我们提出自动化科学后悔最小化（ASMR）——一种自动化计算认知科学框架。基于科学后悔最小化原则，ASMR利用近期提出的认知基础模型Centaur，识别可解释认知模型中的缺陷，并通过基于语言的推理模型自动生成修订方案。我们在多属性决策任务中验证了该方法的有效性，结果表明ASMR发现的认知模型能在噪声天花板水平上预测人类行为，同时保持可解释性。总体而言，该研究展示了ASMR在自动化认知建模流程核心环节中的潜力。
原文摘要 · Abstract (English)
We introduce automated scientific minimization of regret (ASMR) -- a framework for automated computational cognitive science. Building on the principles of scientific regret minimization, ASMR leverages Centaur -- a recently proposed foundation model of human cognition -- to identify gaps in an interpretable cognitive model. These gaps are then addressed through automated revisions generated by a language-based reasoning model. We demonstrate the utility of this approach in a multi-attribute decision-making task, showing that ASMR discovers cognitive models that predict human behavior at noise ceiling while retaining interpretability. Taken together, our results highlight the potential of ASMR to automate core components of the cognitive modeling pipeline.
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