Perception, representation, and memory of ensemble statistics has attracted growing interest. Studies found that, at different abstraction levels, the brain represents similar items as unified percepts. We found that global ensemble perception is automatic and unconscious, affecting later perceptual judgments regarding individual member items. Implicit effects of set mean and range for low-level feature ensembles (size, orientation, brightness) were replicated for high-level category objects. This similarity suggests that analogous mechanisms underlie these extreme levels of abstraction. Here, we bridge the span between visual features and semantic object categories using the identical implicit perception experimental paradigm for intermediate novel visual-shape categories, constructing ensemble exemplars by introducing systematic variations of a central category base or ancestor. In five experiments, with different item variability, we test automatic representation of ensemble category characteristics and its effect on a subsequent memory task. Results show that observer representation of ensembles includes the group’s central shape, category ancestor (progenitor), or group mean. Observers also easily reject memory of shapes belonging to different categories, i.e. originating from different ancestors. We conclude that complex categories, like simple visual form ensembles, are represented in terms of statistics including a central object, as well as category boundaries. We refer to the model proposed by Benna and Fusi (bioRxiv 624239, 2019) that memory representation is compressed when related elements are represented by identifying their ancestor and each one’s difference from it. We suggest that ensemble mean perception, like category prototype extraction, might reflect employment at different representation levels of an essential, general representation mechanism.

集合统计的感知、表示和记忆引起了越来越多的兴趣。研究发现，在不同的抽象层次上，大脑将相似的事物表示为统一的感知。我们发现全局整体感知是自动且无意识的，影响后来对个体成员项目的感知判断。低级特征集合（大小、方向、亮度）的设置均值和范围的隐式影响被复制到高级类别对象。这种相似性表明类似的机制是这些极端抽象级别的基础。在这里，我们使用相同的内隐感知实验范式来弥合视觉特征和语义对象类别之间的跨度，用于中间新颖的视觉形状类别，通过引入中心类别基础或祖先的系统变化来构建整体范例。在五个具有不同项目变异性的实验中，我们测试了集合类别特征的自动表示及其对后续记忆任务的影响。结果表明，集合的观察者表示包括群体的中心形状、类别祖先（祖先）或群体平均值。观察者也很容易拒绝对属于不同类别的形状的记忆，即源自不同祖先的形状。我们得出的结论是，复杂的类别，例如简单的视觉形式集合，是用统计数据来表示的，包括中心对象以及类别边界。我们参考了 Benna 和 Fusi ( bioRxiv 624239, 2019) 提出的模型，当相关元素通过识别其祖先以及每个元素与它的差异来表示时，内存表示会被压缩。 我们认为，整体平均感知，如类别原型提取，可能反映了基本的、通用的表示机制的不同表示级别的使用。