Sebastian Musslick

The paper demonstrates control adjustment costs when people change goals and validates a dynamical systems model explaining these costs through four experiments.

EEG-GAN generates synthetic EEG data to improve classification accuracy across multiple datasets and classifiers, achieving up to 16% improvement in decoding accuracy.

The paper models the cost of control in neural networks executing multiple tasks and derives an optimal control policy based on information-theoretic principles.

This study compares non-human primate and deep reinforcement learning agent performance in a virtual pursuit-avoidance task, finding agents primarily learn first-order physics while the primate exhibits second-order physics.

This paper proposes a framework connecting motivational and cognitive impairments in depression using computational models of cognitive control.

This paper investigates how the brain integrates evidence for different perceptual attributes and responses during decision-making, finding distinct roles for ventromedial prefrontal cortex and dorsal anterior cingulate cortex.