Humans share with many animal species the ability to perceive and approximately represent the number of objects in visual scenes. This ability improves throughout childhood, suggesting that learning and development play a key role in shaping our number sense. This hypothesis is further supported by computational investigations based on deep learning, which have shown that numerosity perception can spontaneously emerge in neural networks that learn the statistical structure of images with a varying number of items. However, neural network models are usually trained using synthetic datasets that might not faithfully reflect the statistical structure of natural environments. In this work, we exploit recent advances in computer vision algorithms to design and implement an original pipeline that can be used to estimate the distribution of numerosity and non-numerical magnitudes in large-scale datasets containing thousands of real images depicting objects in daily life situations. We show that in natural visual scenes the frequency of appearance of different numerosities follows a power law distribution and that numerosity is strongly correlated with many continuous magnitudes, such as cumulative areas and convex hull, which might explain why numerosity judgements are often influenced by these non-numerical cues.

本研究探讨了人类在视觉场景中感知和近似表示物体数量的能力，并提出了一种新颖的计算机视觉算法管道，用于分析包含真实图像的数据集。研究发现，自然视觉场景中不同数量出现的频率遵循幂律分布，并且数量感知与诸如累积面积等连续大小密切相关，这揭示了数量判断受非数量线索影响的潜在原因。

利用计算机视觉估计自然场景中数量和非数量视觉大小的分布