Humans and other animals are able to perceive and represent a number of objects present in a scene, a core cognitive ability thought to underlie the development of mathematics. However, the perceptual mechanisms that underpin this capacity remain poorly understood. Here, we show that our visual sense of number derives from a visual system designed to efficiently encode the location of objects in scenes. Using a mathematical model, we demonstrate that an efficient but information-limited encoding of objects' locations can explain many key aspects of number psychophysics, including subitizing, Weber's law, underestimation, and effects of exposure time. In two experiments (N = 100 each), we find that this model of visual encoding captures human performance in both a change-localization task and a number estimation task. In a third experiment (N = 100), we find that individual differences in change-localization performance are highly predictive of differences in number estimation, both in terms of overall performance and inferred model parameters, with participants having numerically indistinguishable inferred information capacities across tasks. Our results therefore indicate that key psychophysical features of numerical cognition do not arise from separate modules or capacities specific to number, but rather as by-products of lower level constraints on perception. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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视觉中有限的信息处理能力解释了数字心理物理学。


人类和其他动物能够感知和表示场景中存在的许多物体，这是一种核心认知能力，被认为是数学发展的基础。然而，支撑这种能力的感知机制仍然知之甚少。在这里，我们展示了我们对数字的视觉感源自一个旨在有效编码场景中物体位置的视觉系统。使用数学模型，我们证明了对物体位置的有效但信息有限的编码可以解释数字心理物理学的许多关键方面，包括子化、韦伯定律、低估和暴露时间的影响。在两个实验（每个 N = 100）中，我们发现这种视觉编码模型可以捕捉人类在变化定位任务和数字估计任务中的表现。在第三个实验（N = 100）中，我们发现变化定位性能的个体差异可以高度预测数字估计的差异，无论是在整体性能还是推断模型参数方面，参与者在跨任务时具有数字上无法区分的推断信息能力。因此，我们的结果表明，数字认知的关键心理物理特征并非来自特定于数字的单独模块或能力，而是作为较低水平的感知约束的副产品。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。