Khayat Noam, Hochstein Shaul
Life Sciences Institute and Edmond and Lily Safra Center (ELSC) for Brain Research, Hebrew University, 91904, Jerusalem, Israel.
Atten Percept Psychophys. 2019 Nov;81(8):2850-2872. doi: 10.3758/s13414-019-01792-7.
Two cognitive processes have been explored that compensate for the limited information that can be perceived and remembered at any given moment. The first parsimonious cognitive process is object categorization. We naturally relate objects to their category, assume they share relevant category properties, often disregarding irrelevant characteristics. Another scene organizing mechanism is representing aspects of the visual world in terms of summary statistics. Spreading attention over a group of objects with some similarity, one perceives an ensemble representation of the group. Without encoding detailed information of individuals, observers process summary data concerning the group, including set mean for various features (from circle size to face expression). Just as categorization may include/depend on prototype and intercategory boundaries, so set perception includes property mean and range. We now explore common features of these processes. We previously investigated summary perception of low-level features with a rapid serial visual presentation (RSVP) paradigm and found that participants perceive both the mean and range extremes of stimulus sets, automatically, implicitly, and on-the-fly, for each RSVP sequence, independently. We now use the same experimental paradigm to test category representation of high-level objects. We find participants perceive categorical characteristics better than they code individual elements. We relate category prototype to set mean and same/different category to in/out-of-range elements, defining a direct parallel between low-level set perception and high-level categorization. The implicit effects of mean or prototype and set or category boundaries are very similar. We suggest that object categorization may share perceptual-computational mechanisms with set summary statistics perception.
人们已经探索了两种认知过程，它们可以弥补在任何给定时刻能够感知和记忆的有限信息。第一个简洁的认知过程是物体分类。我们自然地将物体与其类别联系起来，假定它们具有相关的类别属性，通常会忽略不相关的特征。另一种场景组织机制是以汇总统计的方式来表征视觉世界的各个方面。将注意力分散到一组具有某种相似性的物体上，人们会感知到这组物体的整体表征。观察者无需编码个体的详细信息，而是处理有关该组的汇总数据，包括各种特征的集合平均值（从圆形大小到面部表情）。正如分类可能包括/依赖于原型和类别间边界一样，集合感知也包括属性平均值和范围。我们现在探讨这些过程的共同特征。我们之前使用快速序列视觉呈现（RSVP）范式研究了低级特征的汇总感知，发现参与者会自动、隐含且即时地为每个RSVP序列独立感知刺激集的平均值和范围极值。我们现在使用相同的实验范式来测试高级物体的类别表征。我们发现参与者对类别特征的感知优于对单个元素的编码。我们将类别原型与集合平均值联系起来，将相同/不同类别与范围内/范围外的元素联系起来，从而在低级集合感知和高级分类之间定义了一种直接的对应关系。平均值或原型以及集合或类别边界的隐含效应非常相似。我们认为物体分类可能与集合汇总统计感知共享感知计算机制。
