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:区分大小数量的能力是人类和动物基本数量能力的一个核心方面。在这项工作中,我们审视了为视觉而设计的最先进的神经网络展示这种基本能力的程度。在动物和婴儿数字认知研究的推动下,我们使用数字分解程序测试神经结构不同家庭的数量差异。我们的结果表明,视像特有感知偏差有助于数字歧视,因为有这种偏差的模型在任务上有最低的测试错误,而且往往具有与执行任务的人类和动物在质量上相似的心理测量曲线。然而,即使是以标准性能衡量的最强模型,在不同的培训和测试条件下,也未能在转移实验中区分数量,表明这种感官偏差可能还不够。</s>