Department of Cognitive Science & Artificial Intelligence, Tilburg University, the Netherlands.
School of Psychological Sciences, University of Melbourne, Australia.
Cogn Psychol. 2019 Jun;111:80-102. doi: 10.1016/j.cogpsych.2019.03.001. Epub 2019 Apr 1.
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.
分类和泛化是两个基本相关的推理问题。然而，分类的主流计算模型（例如，Nosofsky，1986）和泛化的主流计算模型（例如，Tenenbaum 和 Griffiths，2001）对推理应该如何随项目数量的变化而变化做出了截然不同的预测。假设其他条件相同，分类模型预测在一个类别中增加项目数量会增加将新项目分配给该类别的机会；泛化模型预测减少，或随着更多范例的出现而收紧类别。本文研究了这种差异，表明即使任务的所有表面元素保持不变，人们在分类和泛化任务中的表现确实存在质的不同。此外，类别频率对泛化的影响受到关于项目如何抽样的假设的调节。我们表明，这两个模型都不能自然地解释这两种分类和泛化任务的行为模式，并讨论了这些框架的理论扩展，以解释类别频率和抽样假设的重要性。
