Abstract
It is a commonplace in statistics that uncertainty about parameters drives learning. Indeed one of the most influential models of behavioural learning has uncertainty at its heart. However, many popular theoretical models of learning focus exclusively on error, and ignore uncertainty. Here we review the links between learning and uncertainty from three perspectives: statistical theories such as the Kalman filter, psychological models in which differential attention is paid to stimuli with an effect on the speed of learning associated with those stimuli, and neurobiological data on the influence of the neuromodulators acetylcholine and norepinephrine on learning and inference.
