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The paper introduces a methodology to measure intra-modal representational convergence at the single-stimulus level using the Generalized Procrustes Algorithm. By applying this to vision models with different training objectives, the authors selected stimuli based on their intra-modal dispersion (agreement). They found that low intra-modal dispersion strongly modulates and increases alignment between vision and language models, suggesting that stimuli with high agreement within a modality lead to higher cross-modal convergence.
Stimuli that vision models agree on most strongly drive alignment with language models, doubling cross-modal convergence.
Neural networks exhibit a remarkable degree of representational convergence across diverse architectures, training objectives, and even data modalities. This convergence is predictive of alignment with brain representation. A recent hypothesis suggests this arises from learning the underlying structure in the environment in similar ways. However, it is unclear how individual stimuli elicit convergent representations across networks. An image can be perceived in multiple ways and expressed differently using words. Here, we introduce a methodology based on the Generalized Procrustes Algorithm to measure intra-modal representational convergence at the single-stimulus level. We applied this to vision models with distinct training objectives, selecting stimuli based on their degree of alignment (intra-modal dispersion). Crucially, we found that this intra-modal dispersion strongly modulates alignment between vision and language models (cross-modal convergence). Specifically, stimuli with low intra-modal dispersion (high agreement among vision models) elicited significantly higher cross-modal alignment than those with high dispersion, by up to a factor of two (e.g., in pairings of DINOv2 with language models). This effect was robust to stimulus selection criteria and generalized across different pairings of vision and language models. Measuring convergence at the single-stimulus level provides a path toward understanding the sources of convergence and divergence across modalities, and between neural networks and human neural representations.
