Toward automatic constructive learning

Thomas R. Shultz

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Toward automatic constructive learning

机译：走向自动建构性学习

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摘要

Neuroconstructivist modeling can be usefully extended with algorithms that build their own topology and recruit existing knowledge, effectively constructing a hierarchy of network modules. Possible benefits include allowing abilities to emerge naturally, in a way that affords objective study, deeper insights, and more rapid progress, and provides more serious consideration of the implications of constructivism.

机译：可以使用构建自己的拓扑并吸收现有知识的算法有效扩展神经构造函数建模，从而有效地构建网络模块的层次结构。可能的好处包括以客观的研究，更深刻的见识和更快的进步以及使建构主义的含义得到更认真考虑的方式，使能力自然地出现。

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《Behavioral and Brain Sciences》 |2008年第3期|p.344-345|共2页
作者
Thomas R. Shultz;
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作者单位

Department of Psychology, McGill University, Montreal, Quebec H3A 1B1, Canada;

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正文语种 eng
中图分类心理学;
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Toward automatic constructive learning

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