发表机构
University of California, Berkeley; New York University（加州大学伯克利分校; 纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种混合系统，将认知算法发现视为程序精炼问题，结合人类模型与LLM代理，在问题解决任务上提升模型拟合度并揭示行为变异性。
AI 中文摘要
从行为数据中开发算法推理的认知模型是认知科学中的一个核心问题，对现有方法提出了挑战。传统的认知建模方法具有可解释性，并能受益于人类专业知识，但缺乏灵活性和可扩展性。新兴的利用大型语言模型（LLMs）从头生成认知模型的技术具有可扩展性和灵活性，但缺乏人类专业知识的作用，且大多应用于比算法恢复更简单的任务。我们提出了一种混合系统，将认知算法的发现视为程序精炼问题。人类创建的认知模型被表示为概率程序，并提供给一个由LLM代理组成的系统，其任务是：识别模型与行为之间的不匹配；在研究者指定的约束内提出代码级修改；并验证结构保真度。修改会传播到一个概率推理模块，该模块执行潜在变量的推理和数据的似然计算。我们在一个问题解决范式上评估了该流程，该范式暴露了多种认知算法。修订后的模型相对于祖先模型持续提高了模型拟合度，并揭示了一小组反复出现的创新，这些创新捕捉了该任务中有意义的行为变异性。
英文摘要
Developing cognitive models of algorithmic reasoning from behavioral data is a central problem in cognitive science that challenges current methods. Traditional approaches to cognitive modeling are interpretable and benefit from human expertise, but lack flexibility and scalability. Emerging techniques using large language models (LLMs) for de novo generation of cognitive models are scalable and flexible, but lack a role for human expertise and have mostly been applied to simpler tasks than algorithm recovery. We propose a hybrid system that treats discovery of cognitive algorithms as a program refinement problem. Human-created cognitive models are expressed as probabilistic programs and provided to a system of LLM agents with a mandate to: identify mismatches between model and behavior; propose code-level modifications within researcher-specified constraints; and verify structural fidelity. Revisions propagate to a probabilistic inference module that performs inference for latent variables and data likelihood computations. We evaluate the pipeline on human behavior in a problem-solving paradigm that exposes a variety of cognitive algorithms. Revised models consistently improve model fit relative to ancestral models and reveal a small set of recurring innovations that capture meaningful behavioral variability in this task.
