Modulating Cross-Modal Convergence with Single-Stimulus, Intra-Modal Dispersion
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 t...
| Authors | Eghbal A. Hosseini, Brian Cheung, Evelina Fedorenko et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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