Ensemble crowd perception: A viewpoint-invariant mechanism to represent average crowd identity
Ensemble crowd perception: A viewpoint-invariant mechanism to represent average crowd identity
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DOI：
10.1167/14.8.26
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发表时间：
2014-01-01
影响因子：
1.8
通讯作者：
Whitney, David
中科院分区：
文献类型：
--
作者：
Leib, Allison Yamanashi;Fischer, Jason;Whitney, David
个人可以快速准确地判断一组相似项目的平均值，包括低级别（Ariely，2001）和高级别对象（Haberman & Whitney，2007）。然而，到目前为止，还不清楚整体感知是否基于视点不变的对象表示。在这里，我们通过向参与者展示一群顺序呈现的面孔来测试这个问题。每个人群中的面孔数量和每张面孔的视角在每次试验中都不同。这种设计要求参与者将来自多个视角的信息整合到一个整体概念中。参与者报告了人群的平均身份（e。例如，在一个实施例中，家族相似性）使用可调节的、面向前的测试面。我们的研究结果表明，参与者准确地感知到的平均人群身份，即使需要将信息跨多个面的方向。控制实验表明，集成编码的精度并不仅仅取决于参与者观看人群的时间长度。此外，控制分析表明，观察员并没有简单地抽样人群中的一个子集的面孔，而是整合了许多面孔到他们的估计平均人群身份。这些结果表明，集成感知可以在最高水平的对象识别后，3-D视点不变的脸表示。
Individuals can rapidly and precisely judge the average of a set of similar items, including both low-level (Ariely, 2001) and high-level objects (Haberman & Whitney, 2007). However, to date, it is unclear whether ensemble perception is based on viewpoint-invariant object representations. Here, we tested this question by presenting participants with crowds of sequentially presented faces. The number of faces in each crowd and the viewpoint of each face varied from trial to trial. This design required participants to integrate information from multiple viewpoints into one ensemble percept. Participants reported the mean identity of crowds (e. g., family resemblance) using an adjustable, forward-oriented test face. Our results showed that participants accurately perceived the mean crowd identity even when required to incorporate information across multiple face orientations. Control experiments showed that the precision of ensemble coding was not solely dependent on the length of time participants viewed the crowd. Moreover, control analyses demonstrated that observers did not simply sample a subset of faces in the crowd but rather integrated many faces into their estimates of average crowd identity. These results demonstrate that ensemble perception can operate at the highest levels of object recognition after 3-D viewpoint-invariant faces are represented.