Noor Seijdel

The paper investigates how speech clarity affects auditory attention and visual-signal coupling during sentence comprehension using EEG and frequency tagging.

This study investigates how attention affects auditory and visual processing and their integration during multimodal communication using MEG and RIFT.

This study shows that both human visual cortex and deep convolutional neural networks prioritize object background over category in object recognition tasks.

This paper tests if deep residual networks (ResNets) can capture recurrent processing in the human brain using EEG data from a visual masking task, finding deeper networks better explain brain activity.

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 shows that feedback activity in the brain increases during object recognition in complex natural scenes, improving performance, while feed-forward processing suffices for simpler scenes.