The ensemble coding literature suggests the existence of a fast, automatic formation of some ensemble codes. Can statistical representations, such as memory for the central tendency along a particular visual feature dimension, be extracted from information held in the sensory register? Furthermore, can knowledge of early, iconic memory processes be used to determine how central tendency is extracted? We focused on the potential role of visible persistence mechanisms that support temporal integration. We tested whether mean orientation could be accurately recalled from brief visual displays using the successive field task. On separate blocks of trials, participants were asked to report the location of a split element (requiring differentiation of frames), a missing element (requiring integration across frames), and the average orientation of elements pooled across both frames (central tendency recall). Results replicate the expected tradeoff between differentiation and integration performance across inter-frame interval (IFI). In contrast, precision of mean estimates was high and invariant across IFIs. A manipulation of within-frame distributional similarity coupled with simulations using 12 models supported 2-item subsampling. The results argue against the “strategic” interpretation of subsampling since 2-item readout was predicted by information theoretic estimates of STM encoding rate: the 2 items were not from a superset in STM. Most crucially, the results argue against the various early “preattentive/parallel/global pooling” accounts and instead suggest that non-selective readout of information from iconic memory supplies a relatively small amount of item information to STM, and it is only at this point that the computation of ensemble averages begins.

集成编码文献表明存在一些集成编码的快速、自动形成。可以从感觉寄存器中保存的信息中提取统计表示，例如沿特定视觉特征维度的集中趋势的记忆吗？此外，是否可以使用有关早期标志性记忆过程的知识来确定如何提取集中趋势？我们专注于支持时间集成的可见持久性机制的潜在作用。我们测试了是否可以使用连续的现场任务从简短的视觉显示中准确地回忆起平均方向。在不同的试验块中，参与者被要求报告分裂元素的位置（需要区分框架）、缺失的元素（需要跨框架整合）、以及跨两个框架汇集的元素的平均方向（集中趋势回忆）。结果复制了跨帧间隔 (IFI) 的差异化和集成性能之间的预期折衷。相比之下，平均估计的精度在 IFI 中是高且不变的。对帧内分布相似性的操作与使用 12 个模型的模拟相结合，支持 2 项二次抽样。结果反驳了对子采样的“战略”解释，因为 STM 编码率的信息论估计预测了 2 项读数：这 2 项不是来自 STM 中的超集。最关键的是，