Abstract

The ability to quickly learn and generalize is one of the brain’s most impressive feats and recreating it remains a major challenge for modern artificial intelligence research. One of the most mysterious one-shot learning abilities displayed by humans is one-shot perceptual learning, whereby a single viewing experience drastically alters visual perception in a long-lasting manner. Where in the brain one-shot perceptual learning occurs and what mechanisms support it remain enigmatic. Combining psychophysics, 7T fMRI, and intracranial recordings, we identify high-level visual cortex as the most likely neural substrate wherein neural plasticity supports one-shot perceptual learning. We further develop a novel deep neural network model incorporating top-down feedback into a vision transformer, which recapitulates and predicts human behavior. The prior knowledge learnt by this model is highly similar to the neural code in the human high-level visual cortex. These results reveal the neurocomputational mechanisms underlying one-shot perceptual learning in humans.

My role in the project

I designed and built a neuro-inspired vision transformer model with top-down feedback and compared its representations with fMRI data, and its behavior with human behavior.

Citation
@ARTICLE{Hachisuka2026-av,
  title     = "Neural and computational mechanisms underlying one-shot
               perceptual learning in humans",
  author    = "Hachisuka, Ayaka and Shor, Jonathan D and Liu, Xujin Chris and
               Friedman, Daniel and Dugan, Patricia and Saez, Ignacio and Panov,
               Fedor E and Wang, Yao and Doyle, Werner and Devinsky, Orrin and
               Oermann, Eric K and He, Biyu J",
  journal   = "Nature Communications",
  publisher = "Nature Publishing Group",
  volume    =  17,
  number    =  1,
  pages     =  1204,
  month     =  feb,
  year      =  2026,
  doi       = "10.1038/s41467-026-68711-x",
  issn      = "2041-1723,2041-1723",
  language  = "en"
}