Experiments were conducted in which novice participants learned to classify pictures of rocks into real-world, scientifically defined categories. The experiments manipulated the distribution of training instances during an initial study phase, and then tested for correct classification and generalization performance during a transfer phase. The similarity structure of the to-be-learned categories was also manipulated across the experiments. A low-parameter version of an exemplar-memory model, used in combination with a high-dimensional feature-space representation for the rock stimuli, provided good overall accounts of the categorization data. The successful accounts included (a) predicting how performance on individual item types within the categories varied with the distributions of training examples, (b) predicting the overall levels of classification accuracy across the different rock categories, and (c) predicting the patterns of between-category confusions that arose when classification errors were made. The work represents a promising initial step in scaling up the application of formal models of perceptual classification learning to complex natural-category domains. We discuss further steps for making use of the model and its associated feature-space representation to search for effective techniques of teaching categories in the science classroom. (PsycINFO Database Record

中文翻译：

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高维自然科学范畴域中分类学习的示例记忆模型的测试。

进行了实验，新手参与者学会了将岩石图片分类为现实世界中科学定义的类别。实验在初始研究阶段操纵了训练实例的分布，然后在转移阶段测试了正确的分类和泛化性能。在整个实验中还操纵了要学习类别的相似性结构。示例性内存模型的低参数版本与岩石刺激的高维特征空间表示结合使用，为分类数据提供了很好的整体说明。成功的说明包括：（a）预测类别中单个项目类型的性能随培训示例的分布而变化，（b）预测不同岩石类别之间分类准确度的总体水平，以及（c）预测发生分类错误时出现的类别间混淆的模式。这项工作代表了将感知分类学习的形式化模型扩展到复杂的自然类别领域的有希望的第一步。我们讨论了进一步利用该模型及其相关特征空间表示的步骤，以寻找科学课堂中教学类别的有效技术。（PsycINFO数据库记录 这项工作代表了将感知分类学习的形式化模型扩展到复杂的自然类别领域的有希望的第一步。我们讨论了进一步利用该模型及其相关特征空间表示的步骤，以寻找科学课堂中教学类别的有效技术。（PsycINFO数据库记录 这项工作代表了将感知分类学习的形式化模型扩展到复杂的自然类别领域的有希望的第一步。我们讨论了进一步利用该模型及其相关特征空间表示的步骤，以寻找科学课堂中教学类别的有效技术。（PsycINFO数据库记录