Ensemble summary statistics as a basis for rapid visual categorization.

Ensemble summary statistics as a basis for rapid visual categorization.
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集成摘要统计作为快速视觉分类的基础。

DOI:
10.1167/15.4.8
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发表时间:
2015
期刊:
影响因子:
1.8
通讯作者:
I. Utochkin
I. Utochkin
中科院分区:
医学4区
文献类型:
--
作者:
I. Utochkin

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集成汇总统计在高级抽象上表示多个对象——也就是说,不表示单个特征,忽略空间组织。这使得它们对于在空间中混合的不同类型的多个物体的快速视觉分类特别有用。快速分类意味着我们有能力一眼就能判断所有可见的物体是代表不同的类型,还是只是一种类型的变体。这里提出的一个框架指出,类似统计测试的过程可以作为分类的基础。在早期阶段(初级分类),当独立的集合特性沿着单个感官维度分布时,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。当主要类别被分离时,视觉系统要么重复形状测试以识别子类别(深度处理),要么实现平均比较测试以沿着新的维度匹配几个主要类别。快速分类并非没有处理限制;根据这些局限性,讨论了选择性注意在分类中的作用。
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.
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