Uncertainty and learning
Peter Dayan Angela Yu
IETE Journal of Research 49, 171-182.
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.
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