Ensemble perception-the encoding of objects by their group properties-is known to be resistant to outlier noise. However, this resistance is somewhat paradoxical: how can the visual system determine which stimuli are outliers without already having derived statistical properties of the ensemble? A simple solution would be that ensemble perception is not a simple, one-step process; instead, outliers are detected through iterative computations that identify items with high deviance from the mean and reduce their weight in the representation over time. Here we tested this hypothesis. In Experiment 1, we found evidence that outliers are discounted from mean orientation judgments, extending previous results from ensemble face perception. In Experiment 2, we tested the timing of outlier rejection by having participants perform speeded judgments of sets with or without outliers. We observed significant increases in reaction time (RT) when outliers were present, but a decrease compared to no-outlier sets of matched range suggesting that range alone did not drive RTs. In Experiment 3 we tested the timing by which outlier noise reduces over time. We presented sets for variable exposure durations and found that noise decreases linearly over time. Altogether these results suggest that ensemble representations are optimized through iterative computations aimed at reducing noise. The finding that ensemble perception is an iterative process provides a useful framework for understanding contextual effects on ensemble perception. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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异常值悖论：迭代集成编码在剔除异常值中的作用。

集合感知——通过对象的组属性对对象进行编码——已知可以抵抗异常噪声。然而，这种阻力有点自相矛盾：视觉系统如何在没有推导出整体的统计特性的情况下确定哪些刺激是异常值？一个简单的解决方案是，集成感知不是一个简单的、一步到位的过程；相反，异常值是通过迭代计算来检测的，这些计算识别出与均值偏差较大的项目，并随着时间的推移减少它们在表示中的权重。在这里，我们测试了这个假设。在实验 1 中，我们发现了证据表明异常值从平均方向判断中被打折扣，扩展了先前来自整体人脸感知的结果。在实验 2 中，我们通过让参与者对有或没有异常值的集合进行快速判断来测试异常值拒绝的时机。我们观察到当存在异常值时反应时间 (RT) 显着增加，但与匹配范围的无异常值集相比有所减少，这表明范围本身并不能驱动 RT。在实验 3 中，我们测试了异常噪声随时间减少的时间。我们展示了可变曝光持续时间的集合，发现噪声随时间线性降低。总之，这些结果表明集成表示通过旨在减少噪声的迭代计算进行了优化。集成感知是一个迭代过程的发现为理解上下文对集成感知的影响提供了一个有用的框架。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。