The brain’s ability to process temporal information is critical to sensory and motor processing, cognition, learning, and memory. However, the neural mechanisms underlying even the simplest of temporal tasks, like discriminating two sounds of different durations, are still unknown. This is in stark contrast to our thorough knowledge about spatial processing in the brain, for instance how the visual system determines the orientation of a bar. One of the less studied aspects of temporal processing in the brain is related to the internal models of movement. The brain has to deal with a physical body that changes continuously and a physical environment that also changes continuously. In order to optimize behaviour, the brain develops internal models of movement that help predicting the sensory feedback from motor actions. It is known that the brain updates its internal models, for instance when the mechanics of a body part is experimentally modified to prevent or alter certain movements. Are the purely temporal features of those experimental manipulations important for the execution of a motor action or the updating of an internal model of movement? The studies proposed in this project focus on the experimental study and computational modelling of temporal processing in the human brain in the range of hundreds of milliseconds, with special emphasis on the relationship between temporal processing and internal models of movement. My model system is sensorimotor synchronization, the mostly specifically human behaviour that allows us to move in synchrony with an external pacing signal like music. I’ll draw analytical tools from my Physics background, particularly from the theory of Dynamical Systems applied to Neuroscience. My studies will be based on hard measures like reaction times, synchronization errors, and electroencephalographic time series; in perturbation experiments evidencing the most mechanical (physical) aspects of motor control; and the building of computational models with predictive power to interpret the results and propose new experiments.