Jessica Loke

The paper shows that deep neural networks align more closely with human brain responses to texture-synthesized images than to natural or object-only images, suggesting shared texture statistics rather than object recognition drive this alignment.

The paper investigates why deep convolutional neural networks (DCNNs) with higher object recognition accuracy perform worse at predicting EEG responses, finding that EEG signals reflect scene segmentation rather than object-specific representations.

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.