DANIEL H. KAISER

This study shows that rhythmic cortical feedback in the alpha band mediates visual integration during natural vision, as revealed by EEG and fMRI experiments.

The paper decodes imagined natural scenes from alpha band EEG activity, showing that complex visual imagery is represented in alpha rhythms and shares representations with perception.

This study uses EEG and multivariate pattern analysis to show that familiarity with scenes and faces is decoded from neural signals with similar temporal dynamics, suggesting category-independent familiarity encoding.

The paper uses EEG decoding to study how expectations about material behaviors influence neural processing, finding a sequence of predictions from general expectation fulfillment to processing of unexpected behaviors.

This study shows that TMS over the right PFC increases DMN activity during facial expression naming, highlighting interactions between face processing and default mode networks.

This study links aesthetic ratings of natural scenes to spectral MEG responses in alpha and beta bands, showing these responses are distinct from visual descriptors.

The paper uses computational models to predict fMRI patterns during abstract concept processing and shows how concept similarities shape brain representations.

The paper uses EEG and multivariate pattern analysis to show that scene attractiveness is represented in early brain signals within 200ms of vision, with individual preference variation and generalization across scene types.

This study benchmarks three MRI sequences to differentiate feedforward and feedback signals in human V1 cortex, finding SE-BOLD provides the best depth-dependent separation.

The paper investigates whether N300/400 EEG differences from scene-object consistency reflect changes in object representations, finding they instead indicate semantic violations and are task-dependent.

The paper shows that perceived and mentally rotated visual contents are represented in different cortical depth regions of V1, suggesting functional segregation through anatomical compartmentalization.

This study shows that coherent scene structure improves object information extraction in visual cortex during object categorization tasks but not during scene categorization.

The paper shows that unconscious processing of stimuli in human visual cortex varies depending on whether subjective or objective awareness measures are used, with different neural correlates observed for each approach.

This study uses EEG and time-resolved representational similarity analysis to show that facial attractiveness representations emerge within 150-200ms and are individually specific and not explained by other face attributes.

The paper shows that the human brain rapidly sorts visual fragments according to their typical vertical location in the environment, using EEG and representational similarity analysis.

The paper shows that real-world scene structure enables faster extraction of category information from visual brain signals compared to jumbled scenes.

The paper shows that cortical processing is more sensitive to spatial than categorical scene structure, as demonstrated through fMRI and EEG experiments with multivariate decoding.

The paper shows that MEG sensor patterns reflect perceptual but not categorical similarity between animate and inanimate objects, contrasting with fMRI results.

This study uses EEG to show that face identity representations emerge gradually, becoming invariant to sex and image variations by 400ms after stimulus onset.

The paper uses MEG decoding to show that spatial attention emerges later than category-based attention during visual search.

The paper shows that objects in typical visual-field locations overcome suppression more efficiently than atypical ones, suggesting environmental statistics shape visual processing.

The paper shows that objects in typical retinotopic locations are processed more efficiently, as evidenced by earlier and more accurate EEG classification.

The paper investigates how positional regularities in visual scenes are encoded in human visual cortex using fMRI, finding a transition from independent to integrative coding along the posterior-anterior axis.