Utochkin Igor S
J Vis. 2015;15(4):8. doi: 10.1167/15.4.8.
Ensemble summary statistics represent multiple objects on the high level of abstraction-that is, without representing individual features and ignoring spatial organization. This makes them especially useful for the rapid visual categorization of multiple objects of different types that are intermixed in space. Rapid categorization implies our ability to judge at one brief glance whether all visible objects represent different types or just variants of one type. A framework presented here states that processes resembling statistical tests can underlie that categorization. At an early stage (primary categorization), when independent ensemble properties are distributed along a single sensory dimension, the shape of that distribution is tested in order to establish whether all features can be represented by a single or multiple peaks. When primary categories are separated, the visual system either reiterates the shape test to recognize subcategories (in-depth processing) or implements mean comparison tests to match several primary categories along a new dimension. Rapid categorization is not free from processing limitations; the role of selective attention in categorization is discussed in light of these limitations.
集合汇总统计在高度抽象层面上表示多个对象，也就是说，不表示单个特征且忽略空间组织。这使得它们对于快速视觉分类空间中混合的不同类型的多个对象特别有用。快速分类意味着我们有能力在一瞥之间判断所有可见对象是代表不同类型还是仅仅是一种类型的变体。这里提出的一个框架表明，类似于统计检验的过程可能是这种分类的基础。在早期阶段（初级分类），当独立的集合属性沿单一感官维度分布时，会测试该分布的形状，以确定所有特征是否可以由单个或多个峰值表示。当初级类别分开时，视觉系统要么重复形状测试以识别子类别（深入处理），要么进行均值比较测试，以便在新维度上匹配几个初级类别。快速分类并非不受处理限制；鉴于这些限制，讨论了选择性注意在分类中的作用。
