To survive, organisms must deal with many kinds of uncertainty, such as recognising objects given partial or uncertain information, or planning an action (e.g. reaching for a cup) with an uncertain outcome. Recent evidence suggests that the adult nervous system meets these challenges by implementing or approximating principles of Bayesian Decision Theory (BDT), which provides optimal solutions to problems of perception and action under uncertainty. However, it has been unclear when and how the nervous system organises itself for this kind of probabilistic computation. I will present results from recent studies showing that key elements of BDT are not in place until remarkably late in childhood – in perceptual tasks, motor tasks, and brain circuits. These results provide a starting point for investigating how the nervous system masters efficient perception and action under uncertainty.Progress on this problem has important future applications to atypical development, sensory / motor rehabilitation, and the design of intelligent agents who can learn from their environment.