Navigating through the environment evokes complex changes of visual, auditory, vestibular, tactile and motor inputs to the brain. Yet, despite this motion, we perceive the world as a stable reality, maintain an integrated sense of where we are, and are able to act rather effortlessly upon surrounding objects. To date, little is known about how the processes for perception and action are maintained dynamically. This project is concerned with the learning and control strategies for perception and action in real-world accelerating environments. Its basic premise, based on statistical optimality principles, is that perception and action problems are related in many ways, not only at the computational level but also at the neural implementation level. The project aims to understand the computational algorithm and its neural embodiment for estimating the state of the world and selecting the right action in accelerating environments, distinguishing contributions of sensory, motor, and cognitive signals. A multidisciplinary approach will be used to understand the neural solutions at different scales and at different levels of abstraction (i.e. brain, behavior, simulations). In three projects, an experimental paradigm will be exploited in which healthy subjects and patients make perceptual judgments and generate motor actions in an accelerating environment. Guided by a comprehensive optimal control framework, behavioral measures will be combined with imaging (EEG) and neural perturbation (TMS) techniques in healthy subjects as well as sensory deprived subjects and cerebellar patients, to identify (adaptive) mechanisms and internal models for perception and action control under whole-body acceleration. This multi-pronged project will be directed at establishing causal links between spatiotemporal neural activation patterns and dynamic sensorimotor integration, and investigate the extent to which such links depend on the integrity of particular brain areas and sensory systems.
Call for proposal
See other projects for this call