How people represent categories and how those representations change over time is a basic question about human cognition. Previous research has demonstrated that people categorize objects by comparing them to category prototypes in early stages of learning but consider the individual exemplars within each category in later stages. However, these results do not seem consistent with findings in the memory literature showing that it becomes increasingly easier to access representations of general knowledge than representations of specific items over time. Why would one rely more on exemplar-based representations in later stages of categorization when it is more difficult to access these exemplars in memory? To reconcile these incongruities, our study proposed that previous findings on categorization are a result of human participants adapting to a specific experimental environment, in which the probability of encountering an object stays uniform over time. In a more realistic environment, however, one would be less likely to encounter the same object if a long time has passed. Confirming our hypothesis, we demonstrated that under environmental statistics identical to typical categorization experiments the advantage of exemplar-based categorization over prototype-based categorization increases over time, replicating previous research in categorization. In contrast, under realistic environmental statistics simulated by our experiments the advantage of exemplar-based categorization over prototype-based categorization decreases over time. A second set of experiments replicated our results, while additionally demonstrating that human categorization is sensitive to the category structure presented to the participants. These results provide converging evidence that human categorization adapts appropriately to environmental statistics.

人们如何表示类别以及这些表示如何随时间变化是关于人类认知的基本问题。先前的研究表明，人们在学习的早期阶段通过将对象与类别原型进行比较来对对象进行分类，但在后期阶段会考虑每个类别中的个体样本。然而，这些结果似乎与记忆文献中的发现不一致，记忆文献表明，随着时间的推移，访问一般知识的表示比访问特定项目的表示变得越来越容易。当在内存中访问这些样本变得更加困难时，为什么在分类的后期阶段会更多地依赖基于样本的表示呢？为了调和这些不一致之处，我们的研究提出，先前的分类发现是人类参与者适应特定实验环境的结果，在该环境中，遇到物体的概率随着时间的推移保持一致。然而，在更现实的环境中，如果经过很长一段时间，人们就不太可能遇到同一个物体。证实了我们的假设，我们证明，在与典型分类实验相同的环境统计下，基于范例的分类相对于基于原型的分类的优势随着时间的推移而增加，复制了先前的分类研究。相反，在我们的实验模拟的现实环境统计下，基于范例的分类相对于基于原型的分类的优势随着时间的推移而减弱。第二组实验复制了我们的结果，同时还证明了人类分类对呈现给参与者的类别结构很敏感。这些结果提供了一致的证据，表明人类分类适当地适应了环境统计数据。