测量度量：跨模型家族的表征相似性度量的判别能力
Measuring the Measures: Discriminative Capacity of Representational Similarity Metrics Across Model Families
AI总结：
本文通过系统评估不同表征相似性度量的判别能力，揭示了软匹配在跨模型家族分离中的最优表现，为大规模模型和脑比较提供了度量选择依据。
摘要：
表征相似性度量是神经科学和人工智能中的基本工具，但我们在跨模型家族的判别能力系统比较上缺乏系统性的比较。我们引入了一个定量框架，以评估表征相似性度量的判别能力，基于其在不同架构（CNNs、Vision Transformers、Swin Transformers、ConvNeXt）和训练制度（监督 vs 自监督）下区分模型家族的能力。使用三种互补的分离度量——dprime 信号检测理论、轮廓系数和 ROC-AUC，我们系统地评估了常用度量方法（包括 RSA、线性可预测性、Procrustes 和软匹配）的判别能力。我们显示，随着度量方法施加更严格的对齐约束，分离度系统性增加。在映射方法中，软匹配实现了最高的分离度，其次是 Procrustes 对齐和线性可预测性。非拟合方法如 RSA 在不同家族中也表现出强分离度。这些结果提供了通过分离度视角对相似性度量的首次系统比较，澄清了它们的相对敏感性，并指导大规模模型和脑比较中的度量选择。
英文摘要：
Representational similarity metrics are fundamental tools in neuroscience and AI, yet we lack systematic comparisons of their discriminative power across model families. We introduce a quantitative framework to evaluate representational similarity measures based on their ability to separate model families-across architectures (CNNs, Vision Transformers, Swin Transformers, ConvNeXt) and training regimes (supervised vs. self-supervised). Using three complementary separability measures-dprime from signal detection theory, silhouette coefficients and ROC-AUC, we systematically assess the discriminative capacity of commonly used metrics including RSA, linear predictivity, Procrustes, and soft matching. We show that separability systematically increases as metrics impose more stringent alignment constraints. Among mapping-based approaches, soft-matching achieves the highest separability, followed by Procrustes alignment and linear predictivity. Non-fitting methods such as RSA also yield strong separability across families. These results provide the first systematic comparison of similarity metrics through a separability lens, clarifying their relative sensitivity and guiding metric choice for large-scale model and brain comparisons.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
