A Zhejiang University research team proposed using brain signals from humans viewing images to train deep neural networks, saying the method improved few-shot learning and abstract concept recognition in novel contexts by an average of 20.5%. The team also found that larger models improved concrete concept accuracy but reduced abstract concept accuracy.