论文标题
通过有效的电机计划吸引学习抽象结构的抽象结构
Learning abstract structure for drawing by efficient motor program induction
论文作者
论文摘要
人类灵活地解决了与受过训练的问题不同的新问题。这种概括的能力得到了学识渊博的概念，这些概念捕获了在不同问题上常见的结构。在这里，我们制定了一项自然主义的绘画任务，以研究人类如何迅速获得结构化的先验知识。该任务需要根据一组可合理的几何规则来绘制共享基础结构的视觉对象。我们表明，人们自发地学习了支持概括的抽象绘图程序，并提出了学习者如何发现这些可重复使用的绘图程序的模型。该模型在与人类相同的环境中受过训练，并被限制以产生有效的运动动作，发现了新的绘图例程，这些程序将转移到测试对象并类似于人类序列的特征。这些结果表明，指导运动计划归纳的两种原理 - 抽象（忽略特定于对象的细节的一般程序）和组成性（重新组合先前学习的程序） - 是解释人类如何学习结构化的内部表征来指导灵活推理和学习的关键。
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.
