معرفی
Professor Paul Bays is a leading cognitive neuroscientist at the University of Cambridge, holding a professorship in the Department of Psychology within the School of Biological Sciences. He directs the Computational Cognition Group (Bays Lab), where his research focuses on understanding the neural and computational mechanisms underlying visual working memory and perception.
His laboratory employs a diverse range of methodologies including visual psychophysics, extended reality (XR) technology, eye and body movement recordings, mathematical models, and artificial neural networks. Bays Lab collaborates extensively with researchers using brain imaging, recording and stimulation techniques, as well as neuropsychologists studying cognitive aging, mental illness, and neurological disorders.
Professor Bays' research has revolutionized our understanding of visual working memory, demonstrating that memory capacity operates as a flexible resource rather than a fixed number of "slots." His work has shown how this resource is dynamically allocated across visual features and how it influences perception, decision-making, and action. A major focus examines how working memory bridges discrete transitions in visual input during eye movements, allowing for continuous visual experience despite frequent shifts in gaze.
His laboratory has made significant contributions to understanding sensory prediction and motor learning, particularly regarding tactile attenuation (the phenomenon where self-generated touch feels weaker than externally generated touch). Recent work has applied efficient coding principles to explain various perceptual phenomena including weight illusions.
- Research Theme: Adaptive Brain Computations
- Research Theme: Lifelong Brain Development and Brain Ageing
- Research Theme: Brains and Machines
Professor Bays actively supervises PhD students and postdoctoral researchers in the Bays Lab, fostering the next generation of cognitive neuroscientists. His laboratory has developed valuable research tools including the Analogue Report Toolbox for analyzing visual working memory data.
