Uncertainty and Learning

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.

Indexing terms:

• Learning
• Uncertainty
• Attention
• Kalman filter
• Neuromodulation
• Acetylcholine
• Norepinephrine

Additional information

Notes on contributors

Peter Dayan

Peter Dayan did his PhD in the University of Edinburgh, postdocs in University of California, San Diego and the University of Toronto, and taught at MIT in the Department of Brain and Cognitive Sciences, before coming to the Gatsby Computational Neuroscience Unit at University College London.

Angela Jyu

Angela J Yu received the following degrees from MIT in 2000: BS in Computer Science, BS in Brain and Cognitive Sciences, and BS in Mathematics. She is currently working on her PhD in Computational Neuroscience at the Gatsby Computational Neuroscience at University College London.