神经网络模型中学习诱导的类别感知
Learning-induced categorical perception in a neural network model
摘要：
在人类认知中，类间距离的扩大和类内距离的压缩被称为类别感知（CP）。关于CP的原因有几种假设（例如，语言，学习，进化），但没有功能模型。合作伙伴是否是必不可少的分类或只是一个副产品，它还不清楚，但证据是积累，合作伙伴可以通过类别学习。我们提供了一个模型，学习引起的CP作为神经网络中隐藏单元空间中距离的扩展和压缩。描述了当前模型预测CP的基本条件，以及这些条件如何推广到更复杂的分类的线索开始出现。
英文摘要：
In human cognition, the expansion of perceived between-category distances and compression of within-category distances is known as categorical perception (CP). There are several hypotheses about the causes of CP (e.g., language, learning, evolution) but no functional model. Whether CP is essential to categorisation or simply a by-product of it is not yet clear, but evidence is accumulating that CP can be induced by category learning. We provide a model for learning-induced CP as expansion and compression of distances in hidden-unit space in neural nets. Basic conditions from which the current model predicts CP are described, and clues as to how these conditions might generalize to more complex kinds of categorization begin to emerge.
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。
