Faisal Mushtaq

This paper describes a community-based participatory approach involving adolescents to co-design research priorities for a population neuroimaging research programme, identifying mental health and stress as top priorities.

The paper investigates strategic behavior in sensorimotor adaptation tasks and finds that participants often experience an 'Aha!' moment leading to an abrupt shift in strategy.

The paper investigates visuomotor memory by testing transfer across contexts with opposing visual motion, finding that motor memory is tied to planned hand displacement rather than visual feedback.

This study tests whether theta frequency tACS improves working memory but finds no significant effects on performance or theta activity.

This study shows that new motor skills can be learned by reusing existing motor commands rather than generating new ones, using a myoelectric interface task with muscle activation.

The paper demonstrates that hysteresis in human sensorimotor decision-making emerges from a dynamic system learning from action consequences, supported by experiments and a computational model.

The paper investigates whether first-person shooter (FPS) games can serve as a valid tool for studying sensorimotor control by comparing kinematic metrics across different visual feedback conditions.

This paper measures motion-to-photon latency in VR systems using a high-speed camera technique, revealing latency reductions due to motion prediction algorithms.

The paper investigates proactive inhibition in complex goal-directed movements, finding that movement duration and endpoint error are affected, challenging a classical dichotomy of inhibition.

The paper examines how the brain processes selection and execution errors using EEG, finding distinct neural signatures for each type of error.

This study shows that disturbance forces enhance motor learning by reducing entropy, as predicted by the Free Energy Principle, through experiments with human arm movements in force fields.

The paper introduces a framework for creating VR experiments in Unity to study human behavior, demonstrating its ability to simplify experiment development and data collection.