Rips L J, Collins A
Department of Psychology, University of Chicago, Illinois.
J Exp Psychol Gen. 1993 Dec;122(4):468-86. doi: 10.1037//0096-3445.122.4.468.
Many theories of concepts link categorizing to similarity. If a new instance is sufficiently similar to category members, then the instance is likely to be a member itself. However, judged similarity are judged category likelihood sometimes diverge. In these studies, we describe frequency distributions for categories that vary along a single dimension, and ask Ss to rate the similarity, typicality, or category likelihood of instances along this continuum. The average ratings exhibit distinct patterns, with category likelihood depending on the instance's frequency and with similarity depending on distance from the instance to the center of the distribution. Typicality ratings show effects of both frequency and distance. These differences occur for bimodal distributions (Experiments 1 and 2) and for unimodal ones (Experiment 3). They appear both when we present the distributions as histograms and when we imply them in descriptions. We argue that similarity-based models of categorizing are incomplete and may apply mainly to situations in which more definitive information is unavailable.
许多概念理论将分类与相似性联系起来。如果一个新实例与类别成员足够相似，那么该实例很可能本身就是一个成员。然而，判断出的相似性和判断出的类别可能性有时会出现分歧。在这些研究中，我们描述了沿单一维度变化的类别的频率分布，并要求被试对沿着这个连续统的实例的相似性、典型性或类别可能性进行评分。平均评分呈现出不同的模式，类别可能性取决于实例的频率，而相似性取决于实例到分布中心的距离。典型性评分显示出频率和距离的影响。这些差异在双峰分布（实验1和实验2）和单峰分布（实验3）中都会出现。当我们将分布呈现为直方图时以及当我们在描述中暗示它们时，这些差异都会出现。我们认为基于相似性的分类模型是不完整的，可能主要适用于缺乏更明确信息的情况。
