Extremely coarse learning objectives induce human-aligned representations in AI vision models
Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that has guided a decade of computational neuroscience. Research on building brain-aligned models has progressively embraced finer-grained learning ob- jectives, from object classification to contrastive self-supervised objectives that maximize distinc- tions among individual images. Yet the effect of learning-signal granularity on brain alignment remains largely unexamined. Here we systematically investigate how the granularity of a learning signal shapes representational alignment with human vision. We parametrically vary the number of training c...
| Authors | Yash Mehta, Michael F. Bonner |
|---|---|
| Primary category | cs.CV |
| Published | 7 May 2026 |
| Last updated at source | 15 September 2026 |
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