Katharina Dobs

The paper shows that alpha-band EEG activity predicts future motion positions of occluded objects, suggesting the brain anticipates motion during tracking.

This study identifies multidimensional tuning in human visual cortex, showing that category-selective areas encode both subcategory features and cross-category distinctions, supporting both local and global coding.

This study compares face and body processing in humans and deep neural networks, finding both segregated and integrated representations across visual cortex regions.

The paper shows that prior scene context influences rapid face detection by shifting reliance from sensory input to spatial expectations.

This study shows that exposing deep neural networks to diverse facial groups reduces bias and improves cross-group face recognition, aligning with human behavior.

The paper shows prior scene context influences neural dynamics during rapid face detection, with early facilitation and later compensatory processing when context is absent.

This study investigates how the brain processes face identity, age, and gender across time and space using MEG data, revealing temporal hierarchies and task-dependent modulation.

The paper shows that deep neural networks trained for face recognition exhibit behavioral signatures similar to human face perception, suggesting these properties arise from task optimization rather than being unique to faces.

This study shows that deep neural networks spontaneously develop functional specialization for different tasks, similar to the human brain's organization.

This study investigates the time course of face perception by analyzing neural responses to faces and finds that gender and age information emerge before identity information, with familiar faces showing enhanced early processing.