Jan Theeuwes

This study investigates the neural correlates of parallel and serial visual search modes using EEG, showing distinct neural patterns and attentional dynamics under identical visual conditions.

The paper shows that statistical learning reduces attentional capture by directing anticipatory micro-saccades toward high-probability distractor locations, supporting a reactive suppression mechanism.

The paper investigates how bottom-up saliency and top-down goals compete in early visual cortex using EEG and RIFT, showing dynamic suppression of distractors over time.

The paper investigates how attentional templates are represented in human visual cortex during preparation, finding dual formats of non-sensory and sensory-like representations.

The paper investigates whether learned distractor suppression operates in retinotopic or spatiotopic coordinates after saccadic eye movements, finding it operates exclusively in retinotopic coordinates.

The paper investigates how attentional biases are formed in static and dynamic environments, finding that object-based learning allows for object-centered attention while egocentric learning remains viewpoint-dependent.

The paper shows that participants learn to suppress high-probability distractor locations in visual search by allocating anticipatory attentional focus, as evidenced by EEG frequency tagging and alpha power changes.

The paper investigates whether suppression of a high-probability distractor location is proactive or reactive by using EEG and spatial memory tasks to probe attentional priority maps.

The paper shows that early visual cortex suppresses potential distractors based on learned spatial priors, even before stimuli are presented.

The paper uses EEG to decode spatial positions of predicted but omitted events, showing dynamic weight changes in a spatial priority map.

The paper demonstrates a method to visualize history-mediated attentional priority maps using EEG and multivariate pattern analysis during intertrial periods.

The paper investigates the electrophysiological markers of statistically learned attentional enhancement and finds it differs from top-down attention by lacking alpha lateralization but showing increased N2pc amplitudes at high-probability locations.

The paper examines how target expectations influence electrophysiological indices of distractor processing in visual search, showing that distractor positivity (PD) is attenuated when target features vary randomly.

This study investigates how dopamine affects attention and learning in Parkinson's disease patients on and off medication using behavioral and fMRI data.

This study finds that spontaneous eye blink rate (sEBR) predicts individual differences in exploration and exploitation during reinforcement learning, specifically linking lower sEBR to exploitative choices and higher sEBR to exploration.

This study shows that dopaminergic medication in Parkinson’s disease reduces striatal sensitivity to negative outcomes during learning and affects subsequent approach/avoidance behavior.

This study shows that visual cortex activity predicts future value-based choices, suggesting a role for visual regions in supporting striatal bias during decision-making.

The paper uses EEG and alpha power analysis to show that both contingent and non-contingent cues capture attention, with distinct temporal dynamics in raw EEG and alpha band activity.

The paper shows that participants proactively suppress frequent distractor locations in visual search before stimuli are presented, as indicated by alpha oscillations and event-related potentials.

The paper investigates how spatial and feature-based attention interact to modulate spatial sampling in visual cortex, showing their combined effect on visual space resampling.

This study uses pupil responses to track value-based decision-making and reinforcement learning during a task, finding distinct patterns linked to value beliefs and reward prediction errors.