A unified account of numerosity perception
A unified account of numerosity perception
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DOI：
10.1038/s41562-020-00946-0
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发表时间：
2020-09-14
影响因子：
29.9
通讯作者：
Piantadosi, Steven T.
中科院分区：
文献类型：
--
作者：
Cheyette, Samuel J.;Piantadosi, Steven T.
人们可以近乎完美的精度识别四项或更少项的集合中的对象数量，但对于更大的集合，误差呈线性增加。一些研究人员将这种不连续性作为两种不同的表征系统的证据。在这里，我们给出了一个数学推导，表明在有限的信息容量下，这种行为是基数的最佳表示，表明这种行为可以出现在单个系统中。我们的推导预测了观众可获得的信息量将如何影响对大集合和小集合的数量感知。在一系列四个预先注册的实验中(每个实验N=100)，我们改变了参与者在数量估计中可获得的信息量。我们发现，无论是小数量还是大数量，该模型都与人类的表现紧密一致，这意味着有效的表征是人类和动物数字认知关键现象背后的共同起源。Cheyette和Piantadosi提出了一个数字感知模型，并发现数字加工的核心属性可以推导为有记忆限制的最优信息加工。
People can identify the number of objects in sets of four or fewer items with near-perfect accuracy but exhibit linearly increasing error for larger sets. Some researchers have taken this discontinuity as evidence of two distinct representational systems. Here, we present a mathematical derivation showing that this behaviour is an optimal representation of cardinalities under a limited informational capacity, indicating that this behaviour can emerge from a single system. Our derivation predicts how the amount of information accessible to viewers should influence the perception of quantity for both large and small sets. In a series of four preregistered experiments (N = 100 each), we varied the amount of information accessible to participants in number estimation. We find tight alignment between the model and human performance for both small and large quantities, implicating efficient representation as the common origin behind key phenomena of human and animal numerical cognition.Cheyette and Piantadosi present a model of numerosity perception and find that core properties of number processing can be derived as optimal information processing with memory limits.