Categorization and generalization are fundamentally related inference problems. Yet leading computational models of categorization (as exemplified by, e.g., Nosofsky, 1986) and generalization (as exemplified by, e.g., Tenenbaum and Griffiths, 2001) make qualitatively different predictions about how inference should change as a function of the number of items. Assuming all else is equal, categorization models predict that increasing the number of items in a category increases the chance of assigning a new item to that category; generalization models predict a decrease, or category tightening with additional exemplars. This paper investigates this discrepancy, showing that people do indeed perform qualitatively differently in categorization and generalization tasks even when all superficial elements of the task are kept constant. Furthermore, the effect of category frequency on generalization is moderated by assumptions about how the items are sampled. We show that neither model naturally accounts for the pattern of behavior across both categorization and generalization tasks, and discuss theoretical extensions of these frameworks to account for the importance of category frequency and sampling assumptions.

中文翻译：

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样本大小、类别数量和抽样假设：探索分类和泛化之间的一些差异

分类和泛化是根本相关的推理问题。然而，领先的分类计算模型（例如，Nosofsky，1986）和泛化（例如，Tenenbaum 和 Griffiths，2001）对推理应该如何作为项目数量的函数而变化做出了质的不同预测。假设所有其他条件都相同，分类模型预测增加一个类别中的项目数量会增加将新项目分配给该类别的机会；泛化模型通过额外的示例预测减少或类别收紧。本文研究了这种差异，表明即使任务的所有表面元素都保持不变，人们在分类和概括任务中的表现确实有所不同。此外，类别频率对泛化的影响通过关于项目如何采样的假设来缓和。我们表明，这两个模型都不能自然地解释跨分类和泛化任务的行为模式，并讨论这些框架的理论扩展以解释类别频率和采样假设的重要性。