Early theories of categorization assumed that either rules, or prototypes, or exemplars were exclusively used to mentally represent categories of objects. More recently, hybrid theories of categorization have been proposed that variously combine these different forms of category representation. Our research addressed the question of whether there are representational shifts during category learning. We report a series of experiments that tracked how individual subjects generalized their acquired category knowledge to classifying new critical transfer items as a function of learning. Individual differences were observed in the generalization patterns exhibited by subjects, and those generalizations changed systematically with experience. Early in learning, subjects generalized on the basis of single diagnostic dimensions, consistent with the use of simple categorization rules. Later in learning, subjects generalized in a manner consistent with the use of similarity-based exemplar retrieval, attending to multiple stimulus dimensions. Theoretical modeling was used to formally corroborate these empirical observations by comparing fits of rule, prototype, and exemplar models to the observed categorization data. Although we provide strong evidence for shifts in the kind of information used to classify objects as a function of categorization experience, interpreting these results in terms of shifts in representational systems underlying perceptual categorization is a far thornier issue. We provide a discussion of the challenges of making claims about category representation, making reference to a wide body of literature suggesting different kinds of representational systems in perceptual categorization and related domains of human cognition.

中文翻译：

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类别学习过程中是否存在表征变化？

早期的分类理论假设规则、原型或范例都专门用于在心理上表示对象的类别。最近，已经提出了混合分类理论，以各种方式组合这些不同形式的类别表示。我们的研究解决了在类别学习过程中是否存在表征变化的问题。我们报告了一系列实验，这些实验跟踪个体受试者如何将他们获得的类别知识概括为将新的关键迁移项目分类为学习的函数。在受试者表现出的概括模式中观察到个体差异，并且这些概括随着经验而系统地改变。在学习的早期，学科在单一诊断维度的基础上进行了概括，与使用简单的分类规则一致。在学习后期，受试者以与使用基于相似性的示例检索一致的方式进行概括，注意多个刺激维度。通过将规则、原型和示例模型的拟合与观察到的分类数据进行比较，使用理论建模来正式证实这些经验观察。尽管我们提供了强有力的证据来证明用于将对象分类为分类经验的函数的信息种类的变化，但根据感知分类基础的表征系统的变化来解释这些结果是一个更棘手的问题。我们讨论了对类别表示提出声明的挑战，