视觉语言模型在神经心理学测试中表现出广泛的视觉缺陷
Visual Language Models show widespread visual deficits on neuropsychological tests
AI总结：
针对视觉语言模型（VLMs）与人类视觉的潜在鸿沟，本文利用神经心理学工具包通过51项测试评估三种先进VLMs，发现其在低中水平视觉能力存在广泛临床级缺陷，却能实现复杂物体识别。
摘要：
视觉语言模型（VLMs）在视觉推理任务中表现出色，成功解决了需要对图像进行高级理解的大学水平挑战。然而，最近一些关于VLMs难以推理方向、位置、连续性和遮挡等基本视觉概念的报告表明，人类和VLM视觉之间可能存在鸿沟。本文使用神经心理学工具包，系统评估了三种最先进VLMs在不同视觉领域的能力。我们从六个临床和实验测试组中选取51项测试，将主流VLMs的视觉能力与健康成年人的标准表现进行对比。尽管这些模型在直接的物体识别任务中表现优异，但我们发现它们在低水平和中水平视觉能力上存在广泛缺陷，这些缺陷在人类中会被认为具有临床意义。通过经过验证的测试组分析出的这些选择性缺陷表明，人工系统可以在不发展人类无需显式训练的基础视觉概念的情况下实现复杂的物体识别。
英文摘要：
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.
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