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Every day we make decisions based on information captured by multiple senses, and on our internal goals. For example, when crossing the road we need to decide how to coordinate our movements to reach the destination safely and at the right time. To achieve this goal we make a guess about where the car is likely to be while we cross the road, given what we see of its trajectory and the sound it produces. Our estimate of the car’s future location is inevitably imperfect, and is combined with our experience of how fast cars encountered in the past have been travelling. For example, we may know that the car is likely to slow down if we are at a pedestrian crossing. This uncertainty places the problem of estimating the future position of the car and how and when we should move to cross the road in a statistical setting. A practical way to conceive of the problem is that the brain uses “rules of thumb” that approximate statistical methods of incorporating prior knowledge and uncertainty (Bayesian theory). The aim of Centre research is to determine these rules of thumb and how they can be implemented in neural circuitry.