To humans, a robin seems more like a bird than a bird seems like a robin, but does this asymmetry also hold for machine vision? Humans and modern vision models can match each other in accuracy while making systematically different kinds of errors, differing not in how often they fail, but in who gets mistaken for whom. We show these directional confusions reveal distinct inductive biases invisible to accuracy alone. Using matched human and deep neural network responses on a natural-image categorization task under 12 perturbation types, we quantify asymmetry in confusion matrices and link its organization to the geometry of the information--error trade-off - how efficiently, and how gracefully, a system generalizes under distortion. We find that humans exhibit broad but weak asymmetries across many class pairs, whereas deep vision models show sparser, stronger directional collapses into a few dominant categories. Robustness training reduces overall asymmetry magnitude but fails to recover this human-like distributed structure. Generative simulations further show that these two asymmetry organizations shift the trade-off geometry in opposite directions even at matched accuracy, explaining why the same scalar asymmetry score can reflect fundamentally different generalization strategies. Together, these results establish directional confusion structure as a sensitive, interpretable signature of inductive bias that accuracy-based evaluation cannot recover.

翻译：对人类而言，知更鸟更像鸟，但鸟却不如知更鸟典型——这种不对称性在机器视觉中也存在吗？虽然人类和现代视觉模型在准确率上不相上下，但它们的错误类型存在系统性差异；区别不在于失败频率，而在于谁被误认为谁。我们证明，这些方向性混淆揭示了仅靠准确率无法观测到的独特归纳偏好。通过分析12种扰动类型下自然图像分类任务中的人类与深度神经网络匹配反应数据，我们量化了混淆矩阵的不对称性，并将其组织模式与信息-误差权衡的几何结构（即系统在信息失真条件下实现泛化的效率与优雅性）相关联。研究发现，人类在众多类别对中表现出广泛但微弱的不对称性，而深度视觉模型则呈现稀疏且更强的方向性坍塌，集中于少数主导类别。鲁棒性训练虽能降低总体不对称性幅度，但无法恢复这种类人分布式结构。生成式模拟进一步表明，即使准确率完全相同，这两种不对称组织模式会以相反方向改变权衡几何结构，这解释了为何相同标量不对称分数可能反映本质不同的泛化策略。综上，本研究确证方向性混淆结构是比准确率更敏感、更可解释的归纳偏好表征指标。