Department of General Psychology, University of Padova, Via Venezia 12, 35131, Padova, Italy.
Department of Information Engineering, University of Padova, Via Venezia 12, 35131, Padova, Italy.
Psychon Bull Rev. 2021 Feb;28(1):158-168. doi: 10.3758/s13423-020-01801-z.
Both humans and nonhuman animals can exhibit sensitivity to the approximate number of items in a visual array or events in a sequence, and across various paradigms, uncertainty in numerosity judgments increases with the number estimated or produced. The pattern of increase is usually described as exhibiting approximate adherence to Weber's law, such that uncertainty increases proportionally to the mean estimate, resulting in a constant coefficient of variation. Such a pattern has been proposed to be a signature characteristic of an innate "number sense." We reexamine published behavioral data from two studies that have been cited as prototypical evidence of adherence to Weber's law and observe that in both cases variability increases less than this account would predict, as indicated by a decreasing coefficient of variation with an increase in number. We also consider evidence from numerosity discrimination studies that show deviations from the constant coefficient of variation pattern. Though behavioral data can sometimes exhibit approximate adherence to Weber's law, our findings suggest that such adherence is not a fixed characteristic of the mechanisms whereby humans and animals estimate numerosity. We suggest instead that the observed pattern of increase in variability with number depends on the circumstances of the task and stimuli, and reflects an adaptive ensemble of mechanisms composed to optimize performance under these circumstances.
人类和非人类动物都可以表现出对视觉数组中项目或序列中事件的近似数量的敏感性，并且在各种范式中，数量估计或产生的不确定性随着数量的增加而增加。增加的模式通常被描述为近似符合韦伯定律，即不确定性与平均估计值成比例增加，导致变异系数恒定。这种模式被认为是一种先天“数字感”的特征。我们重新检查了两个被引用为遵守韦伯定律的典型证据的已发表的行为数据，并观察到在这两种情况下，可变性的增加都小于该解释所预测的，这表明变异系数随着数量的增加而减少。我们还考虑了数量辨别研究的证据，这些证据显示出与变异系数恒定模式的偏差。尽管行为数据有时可以近似遵守韦伯定律，但我们的发现表明，这种遵守并不是人类和动物估计数量的机制的固定特征。相反，我们认为，随着数量的增加，可变性增加的观察模式取决于任务和刺激的情况，并反映了在这些情况下优化性能的自适应机制组合。
