用大模型生成真实任务，让算法学会适应环境规律。
Meta-learning ecological priors from large language models explains human learning and decision making
- 用大语言模型生成自然任务，再通过元学习训练出适应环境的推理算法。
- 在15项实验中，新算法比多个经典认知模型更准确预测人类行为。
- 适合研究认知科学、智能系统与人类行为建模的学者。
人类认知深受所处环境影响。但学习与决策是否可被解释为对现实任务统计结构的合理适应，仍是开放问题。本文提出生态理性分析，将理性分析的规范基础与生态现实结合。利用大语言模型大规模生成具有生态有效性的认知任务，并通过元学习构建针对此类环境优化的理性模型，提出一种新型学习算法：生态理性元学习推断（ERMI）。ERMI 内化自然问题空间的统计规律，无需人工设计启发式或显式参数更新，即可灵活应对新情境。我们在15项涵盖函数学习、类别学习和决策的任务实验中验证，ERMI 在逐次试验预测上优于多个经典认知模型。结果表明，人类认知的许多方面可能正是对日常遭遇问题生态结构的适应性反应。
原文摘要 · Abstract (English)
Human cognition is profoundly shaped by the environments in which it unfolds. Yet, it remains an open question whether learning and decision making can be explained as a principled adaptation to the statistical structure of real-world tasks. We introduce ecologically rational analysis, a computational framework that unifies the normative foundations of rational analysis with ecological grounding. Leveraging large language models to generate ecologically valid cognitive tasks at scale, and using meta-learning to derive rational models optimized for these environments, we develop a new class of learning algorithms: Ecologically Rational Meta-learned Inference (ERMI). ERMI internalizes the statistical regularities of naturalistic problem spaces and adapts flexibly to novel situations, without requiring hand-crafted heuristics or explicit parameter updates. We show that ERMI captures human behavior across 15 experiments spanning function learning, category learning, and decision making, outperforming several established cognitive models in trial-by-trial prediction. Our results suggest that much of human cognition may reflect adaptive alignment to the ecological structure of the problems we encounter in everyday life.
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