Visual Language Models (VLMs) show remarkable performance in visual reasoning tasks, successfully tackling college-level challenges that require high-level understanding of images. However, some recent reports of VLMs struggling to reason about elemental visual concepts like orientation, position, continuity, and occlusion suggest a potential gulf between human and VLM vision. Here we use the toolkit of neuropsychology to systematically assess the capabilities of three state-of-the-art VLMs across visual domains. Using 51 tests drawn from six clinical and experimental batteries, we characterise the visual abilities of leading VLMs relative to normative performance in healthy adults. While the models excel in straightforward object recognition tasks, we find widespread deficits in low- and mid-level visual abilities that would be considered clinically significant in humans. These selective deficits, profiled through validated test batteries, suggest that an artificial system can achieve complex object recognition without developing foundational visual concepts that in humans require no explicit training.

本研究探讨了视觉语言模型（VLM）在处理基本视觉概念（如方向、位置、连续性和遮挡）时的困境，揭示了它们与人类视觉之间的差距。通过系统地评估三种先进的VLM在视觉领域的能力，发现尽管模型在物体识别任务中表现出色，但在低级和中级视觉能力上存在广泛缺陷，这些缺陷在人类中被视为临床显著。

视觉语言模型在神经心理学测试中显示广泛的视觉缺陷