DANIEL H. KAISER

The paper shows familiarity enhances intersubject synchrony in beta and gamma rhythms during natural viewing, reflecting coordinated processing in visual and default mode networks.

The paper shows that accelerating visual information flow in adults mimics infant vision, reducing fine-grained object distinctions while preserving general object representations.

The paper shows that early EEG responses can predict liking for artworks presented in rapid visual streams, even without explicit liking judgments.

This paper develops a neuro-computational approach to assess mental image qualities using EEG and AI-generated images, finding that blurred and low contrast images align better with human alpha activity.

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

The paper investigates the spatiotemporal dynamics of image beauty and quality perception using EEG and fMRI, finding distinct neural representations for each.

The paper investigates how contextual associations between real-world objects affect attentional blink, finding they improve second-target identification but impair first-target identification.

The paper shows that early visual cortex in humans and non-human primates rapidly computes perceived beauty of objects without explicit judgments.

The paper shows that contextual object associations guide attention in visual search, interacting with categorical information and affecting search performance.

The paper investigates how presentation time and task affect neural representations of scene beauty using EEG, finding they are largely unaffected.

The paper shows that visual features predict aesthetic experiences during movie watching, with color and motion features being most informative across different movies.

The paper investigates how contextual associations shape neural representations of objects in visual cortex, showing that contextual relations emerge late and are encoded in specific brain regions.

The paper tests if individual internal models predict differences in gaze behavior during scene exploration, finding that internal models influence gaze under specific conditions.

This study investigates surround suppression in dynamic natural scenes, finding that suppression increases with decreasing categorical similarity between center and surround, and is stronger when motion directions are congruent.

This study examines how variability in object-category templates affects visual search performance, finding that higher variability leads to slower responses and that individual differences in template variability influence search efficiency.

The paper shows that shared gaze patterns during viewing predict shared aesthetic judgments, even when participants are not actively rating.

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 shows that EEG decoding of visual scenes differs when stimuli transition gradually versus abruptly, with weaker and less structured decoding for gradual transitions.

The paper investigates how motion congruence and categorical similarity between central and surrounding scenes influence surround modulation in different visual areas using fMRI.

The paper analyzes how visual and conceptual factors influence the composition of typical scene drawings using a dataset of 1,192 drawings and shows both factors jointly predict object selection.

The paper investigates the neural time course of size constancy in natural scenes using EEG and MVPA, finding that retinal size decoding begins at 80 ms and distance modulation occurs later.

The paper shows that cortical alpha rhythms interpolate occluded motion in natural scenes based on context.

The paper shows that perception of visual coherence co-varies with shifts in neural activity between feedback-related alpha and feedforward-related gamma rhythms.

The paper investigates how personally typical scenes are more accurately perceived and represented in the brain, showing enhanced neural responses and idiosyncratic representations.

This study investigates how recurrent processing contributes to visual object recognition by analyzing EEG and fMRI data, revealing its role in shaping visual representations across the ventral visual stream.

This study identifies a triple network model involving the language, sensorimotor, and default mode networks during situationally simulated inner speech.

The paper shows that integrative processing in both artificial neural networks and human visual cortex predicts the perceived beauty of natural images.

The paper investigates whether integration-related alpha dynamics are triggered by categorical coherence in visual inputs using EEG experiments and multivariate classification.

The paper investigates how moving and static stimuli from different categories are processed across visual pathways using fMRI, finding differential activation patterns in specific brain regions.