Ensemble representations are often described as efficient tools when summarizing features of multiple similar objects as a group. However, it can sometimes be more useful not to compute a single summary description for all of the objects if they are substantially different, for example when they belong to entirely different categories. It was proposed that the visual system can efficiently use the distributional information of ensembles to decide whether simultaneously displayed items belong to single or several different categories. Here we directly tested how the feature distribution of items in a visual array affects an ability to discriminate individual items (Experiment 1) and sets (Experiments 2-3) when participants were instructed explicitly to categorize individual objects based on the median of size distribution. We varied the width (narrow or fat) as well as the shape (smooth or two-peaked) of distributions in order to manipulate the ease of ensemble extraction from the items. We found that observers unintentionally relied on the grand mean as a natural categorical boundary and that their categorization accuracy increased as a function of the size differences among individual items and a function of their separation from the grand mean. For ensembles drawn from two-peaked size distributions, participants showed better categorization performance. They were more accurate at judging within-category ensemble properties in other dimensions (centroid and orientation) and less biased by superset statistics. This finding corroborates the idea that the two-peaked feature distributions support the "segmentability" of spatially intermixed sets of objects. Our results emphasize important roles of ensemble statistics (mean, range, distribution shape) in explicit visual categorization.

中文翻译：

---

对功能分布在快速视觉分类中扮演的角色的明确研究。

当将多个相似对象的特征汇总为一组时，集合表示通常被描述为有效的工具。但是，有时如果某些对象完全不同（例如，当它们属于完全不同的类别时），则不为所有对象计算单个摘要描述可能会更有用。提出了视觉系统可以有效地使用集合的分布信息来确定同时显示的项目是属于单个类别还是几个不同类别。在这里，我们直接测试了视觉阵列中项目的特征分布如何影响区分单个项目（实验1）和集合（实验2-3）的能力（当明确指示参与者根据大小分布的中位数对单个对象进行分类时）。我们改变了分布的宽度（窄或胖）以及分布的形状（平滑或两峰），以操纵从物品中整体提取的难易程度。我们发现观察者无意间将盛大均值作为自然分类边界，并且其分类精度随着各个项目之间的大小差异以及它们与盛大均值的分离而增加。对于从两个峰值大小分布中得出的合奏，参与者表现出更好的分类性能。它们在判断其他维度（质心和方向）中类别内集合的属性时更准确，并且不受超集统计数据的偏见。这一发现证实了两峰特征分布支持空间混合对象集的“可分割性”的想法。