Humans can readily judge the number of objects in a visual scene, even without counting, and such a skill has been documented in many animal species and babies prior to language development and formal schooling. Numerical judgments are error-free for small sets, while for larger collections responses become approximate, with variability increasing proportionally to the target number. This response pattern is observed for items of all kinds, despite variation in object features (such as color or shape), suggesting that our visual number sense relies on abstract representations of numerosity. Here, we investigate whether large-scale generative Artificial Intelligence (AI) systems have a human-like number sense, which should allow them to reliably name the number of objects in simple visual stimuli or generate images containing a target number of items in the 1-10 range. Surprisingly, most of the foundation models considered have a poor number sense: They make striking errors even with small numbers, the response variability does not increase in a systematic way, and the pattern of errors depends on object category. Only the most recent proprietary systems exhibit signatures of a visual number sense. Our findings demonstrate that having an intuitive visual understanding of number remains challenging for foundation models, which in turn might be detrimental to the perceptual grounding of numeracy that in humans is crucial for mathematical learning.

翻译：人类可以轻易判断视觉场景中的物体数量，甚至无需计数，这种能力在语言发展和正规教育之前的婴儿及许多动物物种中均有记录。对于小规模集合，数量判断毫无差错；而对于较大集合，反应变得近似，变异性随目标数量成比例增加。这种反应模式适用于所有类型的物体，尽管物体特征（如颜色或形状）存在差异，这表明我们的视觉数感依赖于对数量的抽象表征。在此，我们研究大规模生成式人工智能（AI）系统是否具备类似人类的数感——即能够可靠地识别简单视觉刺激中的物体数量，或在1-10范围内生成包含目标数量物体的图像。令人惊讶的是，大多数被考察的基础模型数感较差：即使在处理小数量时也会出现显著错误，反应变异性未呈现系统性增长，且错误模式取决于物体类别。仅最新的专有模型展现出视觉数感的迹象。我们的研究发现表明，对基础模型而言，具备直观的视觉数量理解仍具挑战性，这可能进而损害数感的知觉基础——而这一基础在人类中对于数学学习至关重要。