Devereux Barry J, Tyler Lorraine K, Geertzen Jeroen, Randall Billi
Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK,
Behav Res Methods. 2014 Dec;46(4):1119-27. doi: 10.3758/s13428-013-0420-4.
Theories of the representation and processing of concepts have been greatly enhanced by models based on information available in semantic property norms. This information relates both to the identity of the features produced in the norms and to their statistical properties. In this article, we introduce a new and large set of property norms that are designed to be a more flexible tool to meet the demands of many different disciplines interested in conceptual knowledge representation, from cognitive psychology to computational linguistics. As well as providing all features listed by 2 or more participants, we also show the considerable linguistic variation that underlies each normalized feature label and the number of participants who generated each variant. Our norms are highly comparable with the largest extant set (McRae, Cree, Seidenberg, & McNorgan, 2005) in terms of the number and distribution of features. In addition, we show how the norms give rise to a coherent category structure. We provide these norms in the hope that the greater detail available in the Centre for Speech, Language and the Brain norms should further promote the development of models of conceptual knowledge. The norms can be downloaded at www.csl.psychol.cam.ac.uk/propertynorms.
基于语义属性规范中可用信息的模型极大地丰富了概念表征与处理的理论。这些信息既涉及规范中所产生特征的一致性，也涉及其统计特性。在本文中，我们引入了一组全新的、大量的属性规范，旨在成为一种更灵活的工具，以满足从认知心理学到计算语言学等众多对概念知识表征感兴趣的不同学科的需求。除了提供两名或更多参与者列出的所有特征外，我们还展示了每个标准化特征标签背后相当大的语言差异以及生成每个变体的参与者数量。就特征的数量和分布而言，我们的规范与现存最大的数据集（McRae、Cree、Seidenberg和McNorgan，2005年）具有高度可比性。此外，我们展示了这些规范如何产生连贯的类别结构。我们提供这些规范，希望语音、语言和大脑中心规范中更详细的信息能够进一步推动概念知识模型的发展。这些规范可在www.csl.psychol.cam.ac.uk/propertynorms下载。
