Does Representation Similarity Capture Function Similarity?

Lucas Hayne · Heejung Jung · R. Carter


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Representation similarity metrics are widely used to compare learned representations in neural networks, as is evident in extensive literature investigating metrics that accurately capture information encoded in representations. However, aiming to capture all of the information available in representations may have little to do with what information is actually used by the downstream network. One solution is to experiment with interventions on network function. By ablating groups of units thought to carry information and observing whether those ablations affect network performance, we can focus on an outcome that mechanistically links representations to function. In this paper, we systematically test representation similarity metrics to evaluate their sensitivity to functional changes induced by ablation. We use network performance changes after ablation as a way to measure the influence of representation on function. These measures of function allow us to test how well similarity metrics capture changes in network performance versus changes to linear decodability. Network performance measures index the information used by the downstream network, while linear decoding methods index available information in the representation. We show that all of the tested metrics are more sensitive to decodable features than network performance. When comparing these metrics, Procrustes and CKA outperform regularized CCA-based methods on average. Although Procrustes and CKA outperform on average, these metrics have a diminished advantage when looking at network performance. We provide ablation tests of the utility of different representational similarity metrics. Our results suggest that interpretability methods will be more effective if they are based on representational similarity metrics that have been evaluated using ablation tests.