人类在有限认知资源下，依序列顺序构建可复用抽象知识的机制。
Path-dependent program induction under resource constraints explains human sequence learning
- 用分层适配语法模型模拟记忆与计算约束下的程序归纳过程
- 实验显示学习者在程序边界处反应变慢，回忆错误呈系统性简化
- 该模型能最好解释个体差异，适合认知科学与机器学习交叉研究
人们如何在有限认知资源下，从序列经验中构建抽象且可复用的知识？本文将率失真理论与程序归纳新进展结合，提出分层适配语法（HAG），包含任务内与跨任务的独立局部和全局库，受记忆与计算约束共同调控。仿真表明，相较于固定语法或浅层分块方法，HAG在率失真权衡上表现更优，泛化能力更强。在线旋律序列学习实验中，参与者回忆误差呈现系统性简化，反应时在推断出的程序边界处显著上升。逐次试验拟合显示，分层库模型对回忆与外样本延续选择的个体差异解释力最强，优于所有替代模型。结果表明，结构化学习本质上是受约束的程序归纳，经验顺序决定了未来抽象的构建方式。
原文摘要 · Abstract (English)
How do people build abstract, reusable knowledge from sequential experience under bounded cognitive resources? To answer this question, we integrate rate-distortion theory with recent advances in program induction to describe how prior knowledge shapes which future structures are cheap to encode and easy to discover. We formalize this in a hierarchical Adaptor Grammar (HAG) with distinct local (within-task) and global (across-task) libraries, governed jointly by constraints on memory and computation. In simulations, HAG achieves better rate-distortion trade-offs and stronger generalization than fixed grammars or shallow chunking methods. In an online melodic sequence-learning experiment, participants' recall errors reflected systematic simplifications and reaction times increased at inferred program boundaries. Trial-by-trial fits further showed that hierarchical libraries best explained individual differences in both recall and out-of-sample continuation choices, outperforming all alternative models. These findings cast structured learning as bounded program induction in which the order of experience shapes future abstractions a learner builds.
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