Pieter Verbeke

This study shows humans adapt learning rates at both fast and slow time scales, with the central orbitofrontal cortex involved in representing environment-specific learning rates.

The paper investigates the testing effect using fMRI and computational models, finding that testing enhances memory through predictive learning mechanisms involving reward-related brain areas.

The paper shows humans adaptively choose between flat and hierarchical learning strategies based on environmental complexity.

This study investigates how theta frequency transcranial stimulation affects hierarchical learning by altering low-level learning rates in a probabilistic reversal learning task.

This study shows theta oscillations adjust their peak frequency to optimize cognitive control during tasks.

The paper proposes that working memory limitations arise from time-based binding, a mechanism necessary for flexible cognition, and compares this theory to other resource limitation theories.

This paper evaluates modulation networks for balancing shared and separated task representations, finding that multiplicative adaptive modulation achieves the best performance.

The paper examines theta and alpha neural oscillations during learning, showing they operate at different timescales with distinct power changes.

The paper tests a computational model of midfrontal theta oscillations in rule switching using EEG data from a probabilistic reversal learning task, finding that theta power and phase coupling correlate with negative prediction errors.

This paper proposes a computational framework combining synchronization and reinforcement learning to address the stability-plasticity dilemma in neural processing.