Edward H.F. de Haan

This study shows that recurrent processing is critical for object recognition in complex scenes, as evidenced by behavioral, EEG, and deep learning results.

This paper shows that deeper convolutional neural networks better distinguish objects from backgrounds in scenes, suggesting that scene segmentation can emerge from network depth without explicit mechanisms.

The paper uses multi-task deep neural networks to analyze visual pathway organization, finding that unrelated tasks lead to less feature sharing in higher layers.