人类解复杂拼图时会动态构建可复用的中间结构，提升解题效率。
Online library learning in human visual puzzle solving
- 通过创建可复用的中间构造来简化复杂问题。
- 经验积累后更高效地选择使用中间构造，减少无效操作。
- 适合研究人机协作与认知建模的读者关注。
学习新复杂任务时，人们常形成可复用的抽象，即使对未来不确定。我们在视觉拼图任务中研究这一过程：参与者定义并重用“助手”——捕捉重复结构的中间构造。在线实验中，参与者逐步解决难度递增的拼图。初期创建大量助手，更注重完整性而非效率；随经验积累，助手使用变得更具选择性与效率，体现出对复用价值与代价的敏感。拥有助手使参与者能解决原本困难或不可能完成的拼图。计算模型显示，人类决策时间与完成拼图的操作数，随程序归纳模型估算的搜索空间增大而增加；而原始程序长度仅预测失败，不反映努力程度。结果表明，在线库学习是人类问题求解的核心机制，支持个体在任务需求增长时灵活构建、优化与复用抽象。
原文摘要 · Abstract (English)
When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future. We study this process in a visual puzzle task where participants define and reuse helpers -- intermediate constructions that capture repeating structure. In an online experiment, participants solved puzzles of increasing difficulty. Early on, they created many helpers, favouring completeness over efficiency. With experience, helper use became more selective and efficient, reflecting sensitivity to reuse and cost. Access to helpers enabled participants to solve puzzles that were otherwise difficult or impossible. Computational modelling shows that human decision times and number of operations used to complete a puzzle increase with search space estimated by a program induction model with library learning. In contrast, raw program length predicts failure but not effort. Together, these results point to online library learning as a core mechanism in human problem solving, allowing people to flexibly build, refine, and reuse abstractions as task demands grow.
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