Biederman I, Subramaniam S, Bar M, Kalocsai P, Fiser J
University of Southern California, Los Angeles 90089-2520, USA.
Psychol Res. 1999;62(2-3):131-53. doi: 10.1007/s004260050047.
The classification of a table as round rather than square, a car as a Mazda rather than a Ford, a drill bit as 3/8-inch rather than 1/4-inch, and a face as Tom have all been regarded as a single process termed "subordinate classification." Despite the common label, the considerable heterogeneity of the perceptual processing required to achieve such classifications requires, minimally, a more detailed taxonomy. Perceptual information relevant to subordinate-level shape classifications can be presumed to vary on continua of (a) the type of distinctive information that is present, nonaccidental or metric, (b) the size of the relevant contours or surfaces, and (c) the similarity of the to-be-discriminated features, such as whether a straight contour has to be distinguished from a contour of low curvature versus high curvature. We consider three, relatively pure cases. Case 1 subordinates may be distinguished by a representation, a geon structural description (GSD), specifying a nonaccidental characterization of an object's large parts and the relations among these parts, such as a round table versus a square table. Case 2 subordinates are also distinguished by GSDs, except that the distinctive GSDs are present at a small scale in a complex object so the location and mapping of the GSDs are contingent on an initial basic-level classification, such as when we use a logo to distinguish various makes of cars. Expertise for Cases 1 and 2 can be easily achieved through specification, often verbal, of the GSDs. Case 3 subordinates, which have furnished much of the grist for theorizing with "view-based" template models, require fine metric discriminations. Cases 1 and 2 account for the overwhelming majority of shape-based basic- and subordinate-level object classifications that people can and do make in their everyday lives. These classifications are typically made quickly, accurately, and with only modest costs of viewpoint changes. Whereas the activation of an array of multiscale, multiorientation filters, presumed to be at the initial stage of all shape processing, may suffice for determining the similarity of the representations mediating recognition among Case 3 subordinate stimuli (and faces), Cases 1 and 2 require that the output of these filters be mapped to classifiers that make explicit the nonaccidental properties, parts, and relations specified by the GSDs.
将桌子归类为圆形而非方形，将汽车归类为马自达而非福特，将钻头归类为3/8英寸而非1/4英寸，以及将面孔识别为汤姆，都被视为一个单一的过程，称为“从属分类”。尽管有共同的标签，但实现此类分类所需的感知处理存在相当大的异质性，这至少需要一个更详细的分类法。与从属层级形状分类相关的感知信息可以假定在以下几个连续统上变化：（a）存在的独特信息的类型，非偶然的或度量的；（b）相关轮廓或表面的大小；（c）待区分特征的相似性，例如直线轮廓是否必须与低曲率轮廓与高曲率轮廓区分开来。我们考虑三种相对纯粹的情况。情况1中，从属类别可以通过一种表征，即geon结构描述（GSD）来区分，它指定了物体大部件的非偶然特征以及这些部件之间的关系，例如圆桌与方桌。情况2中的从属类别同样由GSD区分，不同的是独特的GSD以小规模存在于复杂物体中，因此GSD的位置和映射取决于最初的基本层级分类，例如当我们使用标志来区分不同品牌的汽车时。对于情况1和情况2的专业知识可以通过通常用语言对GSD的指定轻松获得。情况3中的从属类别为基于“视图”的模板模型的理论化提供了大量素材，需要精细的度量区分。情况1和情况2涵盖了人们在日常生活中能够且确实进行的绝大多数基于形状的基本层级和从属层级物体分类。这些分类通常快速、准确地进行，并且视角变化的成本适中。虽然假定在所有形状处理的初始阶段激活一系列多尺度、多方向滤波器可能足以确定介导情况3中从属刺激（和面孔）识别的表征的相似性，但情况1和情况2要求将这些滤波器的输出映射到分类器，这些分类器明确GSD指定的非偶然属性、部件和关系。
