Categories and resemblance.
Categories and resemblance.
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DOI：
10.1037/0096-3445.122.4.468
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发表时间：
1993-12
期刊：
影响因子：
--
通讯作者：
L. Rips;A. Collins
中科院分区：
文献类型：
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作者：
L. Rips;A. Collins
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，那么该实例本身很可能就是一个成员。然而，判断的相似性和判断的类别似然有时会出现分歧。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Ss沿着这个连续体对实例的相似性、典型性或类别可能性进行评级。平均评分表现出不同的模式，类别可能性取决于实例的频率，相似度取决于实例到分布中心的距离。典型性评级显示频率和距离的影响。这些差异发生在双峰分布（实验1和2）和单峰分布（实验3）中。当我们将分布表示为直方图时，以及当我们在描述中暗示它们时，它们都会出现。我们认为，基于相似性的分类模型是不完整的，可能主要适用于无法获得更明确信息的情况。
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.