Learning list concepts through program induction
Learning list concepts through program induction
复制标题
通过程序归纳学习列表概念
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Tenenbaum
中科院分区：
文献类型：
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作者：
Joshua S. Rule;Eric Schulz;S. Piantadosi;J. Tenenbaum
人类掌握了复杂的相互关联的概念系统，如数学和自然语言。先前的研究表明，学习这些系统依赖于迭代和直接修改类似语言的概念表示。我们介绍并评估了一种新的概念学习范式，称为玛莎的神奇机器，它捕捉了概念之间的复杂关系。在这个范例中，我们将人类概念学习建模为术语重写系统空间中的搜索，而术语重写系统以前是作为抽象的计算模型开发的。我们的模型准确地预测，参与者学习一些转换比其他人更容易，他们学习更难的概念更容易使用一个引导课程集中在他们的组成部分。我们的研究结果表明，术语重写系统可能是一个有用的人类概念表征模型。
Humans master complex systems of interrelated concepts like mathematics and natural language. Previous work suggests learning these systems relies on iteratively and directly revising a language-like conceptual representation. We introduce and assess a novel concept learning paradigm called Martha’s Magical Machines that captures complex relationships between concepts. We model human concept learning in this paradigm as a search in the space of term rewriting systems, previously developed as an abstract model of computation. Our model accurately predicts that participants learn some transformations more easily than others and that they learn harder concepts more easily using a bootstrapping curriculum focused on their compositional parts. Our results suggest that term rewriting systems may be a useful model of human conceptual representations.
DOI：
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发表时间：
2015
期刊：
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影响因子：
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作者：
Laurence, S.;Margolis, E.
通讯作者：
Margolis, E.