Our goal is to understand and optimize human concept learning by predicting the ease of learning of a particular exemplar or category. We propose a method for estimating ease values, quantitative measures of ease of learning, as an alternative to conducting costly empirical training studies. Our method combines a psychological embedding of domain exemplars with a pragmatic categorization model. The two components are integrated using a radial basis function network (RBFN) that predicts ease values. The free parameters of the RBFN are fit using human similarity judgments, circumventing the need to collect human training data to fit more complex models of human categorization. We conduct two category-training experiments to validate predictions of the RBFN. We demonstrate that an instance-based RBFN outperforms both a prototype-based RBFN and an empirical approach using the raw data. Although the human data were collected across diverse experimental conditions, the predicted ease values strongly correlate with human learning performance. Training can be sequenced by (predicted) ease, achieving what is known as fading in the psychology literature and curriculum learning in the machine-learning literature, both of which have been shown to facilitate learning.

中文翻译：

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使用径向基函数网络预测人类类别学习的难易程度

我们的目标是通过预测特定样本或类别的学习难易程度来理解和优化人类概念学习。我们提出了一种估计易用性值的方法，即学习难易度的定量度量，作为进行昂贵的经验培训研究的替代方法。我们的方法将领域样本的心理嵌入与实用的分类模型相结合。这两个组件使用径向基函数网络 (RBFN) 进行集成，该网络可预测缓动值。RBFN 的自由参数使用人类相似性判断进行拟合，避免了收集人类训练数据以拟合更复杂的人类分类模型的需要。我们进行了两个类别训练实验来验证 RBFN 的预测。我们证明基于实例的 RBFN 优于基于原型的 RBFN 和使用原始数据的经验方法。尽管人类数据是在不同的实验条件下收集的，但预测的易用性值与人类学习表现密切相关。训练可以按（预测的）容易程度进行排序，实现心理学文献中所谓的衰落和机器学习文献中的课程学习，这两者都已被证明有助于学习。