Estimating the distribution of numerosity and non-numerical visual magnitudes in natural scenes using computer vision

TL;DR

This study uses advanced computer vision techniques to analyze natural images, revealing that the distribution of object counts follows a power law and that visual cues are consistently correlated across different scene types, informing our understanding of numerosity perception.

Contribution

The paper introduces a novel pipeline leveraging computer vision to estimate numerosity and visual magnitudes in large-scale natural image datasets, bridging ecological validity and computational modeling.

Findings

Numerosity in natural scenes follows a power law distribution.

Correlations between numerosity and visual magnitudes are stable across datasets.

Ecological patterns of covariance influence numerosity judgments.

Abstract

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, and there is also growing interest in using more ecological visual stimuli to investigate numerosity perception in humans. 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. Moreover, we show that the correlational structure for numerosity and continuous magnitudes is stable across datasets and scene types (homogeneous vs. heterogeneous object sets). We suggest that considering such "ecological" pattern of covariance is important to understand the influence of non-numerical visual cues on numerosity judgements.