Categorical perception refers to the enhancement of perceptual sensitivity near category boundaries, generally along dimensions that are informative about category membership. However, it remains unclear exactly which dimensions are treated as informative and why. This article reports a series of experiments in which subjects were asked to learn statistically defined categories in a novel, unfamiliar 2D perceptual space of shapes. Perceptual discrimination was tested before and after category learning of various features in the space, each defined by its position and orientation relative to the maximally informative dimension. The results support a remarkably simple generalization: The magnitude of improvement in perceptual discrimination of each feature is proportional to the mutual information between the feature and the category variable. This finding suggests a rational basis for categorical perception in which the precision of perceptual discrimination is tuned to the statistical structure of the environment.

分类感知是指在类别边界附近增强感知灵敏度，通常沿着有关类别成员信息的维度。然而，目前尚不清楚哪些维度被视为提供信息以及为什么。本文报道了一系列实验，在这些实验中，受试者被要求在一个新颖的、不熟悉的 2D 形状感知空间中学习统计定义的类别。在空间中各种特征的类别学习之前和之后测试感知辨别力，每个特征都由其相对于最大信息维度的位置和方向定义。结果支持一个非常简单的概括：每个特征的感知辨别的改进幅度与特征和类别变量之间的互信息成正比。