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ARXIV:2604.21836 · CROSS-MODAL REPRESENTATION · SUBMITTED 24 APR · 20:32 UTC · FRESHNESS STALE
ARXIV:2604.21836CROSS-MODAL REPRESENTATIONSUBMITTED 24 APR · 20:32 UTCFRESHNESS STALEEghbal A. Hosseini · Brian Cheung · Evelina Fedorenko · Alex H. Williams · arXiv
A methodology using Generalized Procrustes Algorithm to measure intra-modal representational convergence at the single-stimulus level, modulating cross-modal convergence between vision and language models.
Opportunity summary
Pain A methodology using Generalized Procrustes Algorithm to measure intra-modal representational convergence at the single-stimulus level, modulating cross-modal convergence between vision and language models.
Evidence 0 refs | 4 sources | 67% coverage
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A methodology using Generalized Procrustes Algorithm to measure intra-modal representational convergence at the single-stimulus level, modulating cross-modal convergence between vision and language models. This convergence is predictive of alignment with brain representation.
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.
ScienceToStartup currently rates this 0.0/10 on the public viability pass. 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…
Cross-Modal Representation moved forward this cycle; last verified April 2026. Public score 0.0/10. Implementation evidence is present through a linked repository.
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A methodology using Generalized Procrustes Algorithm to measure intra-modal representational convergence at the single-stimulus level, modulating cross-modal convergence between vision and language models.
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10.48550/arXiv.2604.21836A methodology using Generalized Procrustes Algorithm to measure intra-modal representational convergence at the single-stimulus level, modulating cross-modal convergence between vision and language models.
Abstract
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.
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A methodology using Generalized Procrustes Algorithm to measure intra-modal representational convergence at the single-stimulus level, modulating cross-modal convergence between vision and language models. This convergence is predictive of alignment with brain representation.
METHOD
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.
RESULT
ScienceToStartup currently rates this 0.0/10 on the public viability pass. 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 representa...
WHY NOW
Cross-Modal Representation moved forward this cycle; last verified April 2026. Public score 0.0/10. Implementation evidence is present through a linked repository.
{"file name": "input.pdf", "number of pages": 7, "author": "Eghbal A. Hosseini; Brian Cheung; Evelina Fedorenko; Alex H. Williams", "title": "Modulating Cross-Modal Convergence with Single-Stimulus
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A methodology using Generalized Procrustes Algorithm to measure intra-modal representational convergence at the single-stimulus level, modulating cross-modal convergence between vision and language models.
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