用高效电机程序归纳法学习抽象结构绘图
Learning abstract structure for drawing by efficient motor program induction
摘要：
人类灵活地解决新问题，这些问题与他们接受的训练有本质上的不同。这种概括能力得到了学习概念的支持，这些概念捕捉了不同问题的共同结构。在这里，我们开发了一个自然主义的绘画任务，研究人类如何快速获得结构化的先验知识。该任务要求基于一组可组合的几何规则绘制共享底层结构的视觉对象。我们表明，人们自发地学习抽象的绘图程序，支持泛化，并提出了一个模型，学习者如何发现这些可重用的绘图程序。在与人类相同的环境中训练，并限制产生有效的运动动作，该模型发现了新的绘图例程，这些例程转移到测试对象，并类似于人类序列的学习特征。这些结果表明，模型中指导运动程序诱导的两个原则-抽象性（忽略特定对象细节的一般程序）和组合性（重新组合先前学习的程序）-是解释人类如何学习指导灵活推理和学习的结构化内部表征的关键。
英文摘要：
Humans flexibly solve new problems that differ qualitatively from those they were trained on. This ability to generalize is supported by learned concepts that capture structure common across different problems. Here we develop a naturalistic drawing task to study how humans rapidly acquire structured prior knowledge. The task requires drawing visual objects that share underlying structure, based on a set of composable geometric rules. We show that people spontaneously learn abstract drawing procedures that support generalization, and propose a model of how learners can discover these reusable drawing programs. Trained in the same setting as humans, and constrained to produce efficient motor actions, this model discovers new drawing routines that transfer to test objects and resemble learned features of human sequences. These results suggest that two principles guiding motor program induction in the model - abstraction (general programs that ignore object-specific details) and compositionality (recombining previously learned programs) - are key for explaining how humans learn structured internal representations that guide flexible reasoning and learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
