Handling imperfect information problems is fundamental to perception, learning, and decision-making. Ensemble perception may partially overcome imperfect information by providing global clues. However, if not all cluster elements are readily accessible, the observations required for computing statistics are incomplete. In this case, these elements' internal correlations (i.e., regularity) could serve as clues to elucidate the missing pieces. We thus investigated spatial regularity's role in ensemble perception under imperfect information situations created using partially occluded stimuli. In two experiments, we manipulated circle size (Experiment 1) and line orientation (Experiment 2) to linearly vary with its location; spatial regularity thus supplied clues for inferring information of the invisible parts. Participants estimated the mean of the targeted feature of the entire cluster, including visible and invisible parts. We observed robust biases toward the overall cluster in the estimations, implying the invisible parts were considered during ensemble perception. We proposed this effect could be understood as assessing evidence from visible parts to construct the missing parts. Experiment 3 employed a periodicity regularity to deter participants from using specific strategies, and consistent results were found. We then developed a generative model, the Regularity-Based Model, to simulate the inference process, which better captured the pattern of human outcomes than the comparative model. These findings indicate the visual system could use high-level structural information to infer scenes with incomplete information, thus producing more accurate ensemble representations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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不完整性中的完整性：从不完美信息集合中的规律性推断。

处理不完美信息问题是感知、学习和决策的基础。整体感知可以通过提供全局线索来部分克服不完美的信息。然而，如果并非所有簇元素都易于访问，则计算统计数据所需的观察结果是不完整的。在这种情况下，这些元素的内部相关性（即规律性）可以作为阐明缺失部分的线索。因此，我们研究了在使用部分遮挡刺激创建的不完美信息情况下空间规律性在整体感知中的作用。在两个实验中，我们操纵圆的大小（实验 1）和线方向（实验 2）随其位置线性变化；因此，空间规律性为推断不可见部分的信息提供了线索。参与者估计整个集群目标特征的平均值，包括可见和不可见的部分。我们在估计中观察到对整个集群的强烈偏差，这意味着在整体感知过程中考虑了不可见的部分。我们提出这种效应可以理解为评估可见部分的证据来构建缺失的部分。实验 3 采用周期性规律来阻止参与者使用特定策略，并且发现了一致的结果。然后，我们开发了一个生成模型，即基于规则的模型，来模拟推理过程，它比比较模型更好地捕捉了人类结果的模式。这些发现表明视觉系统可以使用高级结构信息来推断信息不完整的场景，从而产生更准确的整体表示。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。