Dry Matthew J, Storms Gert
University of Leuven, Department of Psychology, Belgium.
Acta Psychol (Amst). 2010 Mar;133(3):244-55. doi: 10.1016/j.actpsy.2009.12.005. Epub 2010 Jan 6.
Many real-world categories contain graded structure: certain category members are rated as more typical or representative of the category than others. Research has shown that this graded structure can be well predicted by the degree of commonality across the feature sets of category members. We demonstrate that two prominent feature-based models of graded structure, the family resemblance (Rosch & Mervis, 1975) and polymorphous concept models (Hampton, 1979), can be generalized via the contrast model (Tversky, 1977) to include both common and distinctive feature information, and apply the models to the prediction of typicality in 11 semantic categories. The results indicate that both types of feature information play a role in the prediction of typicality, with common features weighted more heavily for within-category predictions, and distinctive features weighted more heavily for contrast-category predictions. The same pattern of results was found in additional analyses employing rated goodness and exemplar generation frequency. It is suggested that these findings provide insight into the processes underlying category formation and representation.
某些类别成员被认为比其他成员更具典型性或代表性。研究表明，这种等级结构可以通过类别成员特征集之间的共性程度得到很好的预测。我们证明，两种著名的基于特征的等级结构模型，家族相似性模型（Rosch & Mervis，1975）和多态概念模型（Hampton，1979），可以通过对比模型（Tversky，1977）进行推广，以纳入共同特征和独特特征信息，并将这些模型应用于预测11个语义类别的典型性。结果表明，这两种类型的特征信息在典型性预测中都发挥了作用，共同特征在类别内预测中权重更大，独特特征在对比类别预测中权重更大。在使用评定的良好程度和范例生成频率的额外分析中也发现了相同的结果模式。有人认为，这些发现为类别形成和表征的潜在过程提供了见解。
