The ability to discriminate between large and small quantities is a core aspect of basic numerical competence in both humans and animals. In this work, we examine the extent to which the state-of-the-art neural networks designed for vision exhibit this basic ability. Motivated by studies in animal and infant numerical cognition, we use the numerical bisection procedure to test number discrimination in different families of neural architectures. Our results suggest that vision-specific inductive biases are helpful in numerosity discrimination, as models with such biases have lowest test errors on the task, and often have psychometric curves that qualitatively resemble those of humans and animals performing the task. However, even the strongest models, as measured on standard metrics of performance, fail to discriminate quantities in transfer experiments with differing training and testing conditions, indicating that such inductive biases might not be sufficient.

翻译：区分大数量与小数量是人类和动物基本数值能力的一个核心方面。本研究探讨了最先进的视觉神经网络在多大程度上展现出这一基本能力。受动物和婴儿数值认知研究的启发，我们采用数值二分法程序来测试不同神经网络架构族的数字辨别能力。我们的结果表明，视觉特定的归纳偏置有助于数量辨别：具有此类偏置的模型在该任务上表现出最低的测试误差，其心理测量曲线在定性上与人类和动物执行该任务时的曲线相似。然而，即便是根据标准性能指标衡量的最强模型，在训练和测试条件不同的迁移实验中，也无法区分数量，这表明此类归纳偏置可能并不足够。