Lau Jonas Sin-Heng, Brady Timothy F
Department of Psychology, University of California, San Diego, La Jolla, CA, USA.
J Vis. 2018 Sep 4;18(9):3. doi: 10.1167/18.9.3.
People can quickly and accurately compute not only the mean size of a set of items but also the size variability of the items. However, it remains unknown how these statistics are estimated. Here we show that neither parallel access to all items nor random subsampling of just a few items is sufficient to explain participants' estimations of size variability. In three experiments, we had participants compare two arrays of circles with different variability in their sizes. In the first two experiments, we manipulated the congruency of the range and variance of the arrays. The arrays with congruent range and variability information were judged more accurately, indicating the use of range as a proxy for variability. Experiments 2B and 3 showed that people also are not invariant to low- or mid-level visual information in the arrays, as comparing arrays with different low-level characteristics (filled vs. outlined circles) led to systematic biases. Together, these experiments indicate that range and low- or mid-level properties are both utilized as proxies for variability discrimination, and people are flexible in adopting these strategies. These strategies are at odds with the claim of parallel extraction of ensemble statistics per se and random subsampling strategies previously proposed in the literature.
人们不仅能够快速、准确地计算一组物品的平均大小，还能计算这些物品大小的变异性。然而，这些统计量是如何被估计的仍然未知。在这里，我们表明，并行访问所有物品或仅对少数物品进行随机子采样都不足以解释参与者对大小变异性的估计。在三个实验中，我们让参与者比较两组大小变异性不同的圆形阵列。在前两个实验中，我们操纵了阵列范围和方差的一致性。范围和变异性信息一致的阵列被判断得更准确，这表明人们使用范围作为变异性的替代指标。实验2B和3表明，人们对阵列中的低或中级视觉信息也并非无动于衷，因为比较具有不同低级别特征（实心圆与空心圆）的阵列会导致系统性偏差。总之，这些实验表明，范围以及低或中级属性都被用作变异性辨别指标，并且人们在采用这些策略时具有灵活性。这些策略与文献中先前提出的并行提取总体统计量本身以及随机子采样策略的观点不一致。
