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Peter Dayan's work has been influential in several fields impinging on [[Cognition|cognitive science]], including [[Learning|machine learning]], [https://en.wikipedia.org/wiki/Mathematical_statistics mathematical statistics], [https://en.wikipedia.org/wiki/Neuroscience neuroscience] and [[Psychology|psychology]] - he has articulated a view in which [[Neural Networks|neural computation]] is akin to a [https://en.wikipedia.org/wiki/Bayesian_inference Bayesian inference] process <ref>[https://cognitivesciencesociety.org/rumelhart-prize/ 2012 Recipient Peter Dayan] | [https://en.wikipedia.org/wiki/Rumelhart_Prize The David E. Rumelhart Prize 2012]</ref>. His research centers around [[Supervised Learning|self-supervised learning]], [[Reinforcement Learning|reinforcement learning]], [[Temporal Difference Learning|temporal difference learning]] and , [https://en.wikipedia.org/wiki/Neural_coding#Population_coding population coding] and [[Monte-Carlo Tree Search|Monte-Carlo tree search]]. He researched and published on [https://en.wikipedia.org/wiki/Q-learning Q-learning] with [[Chris Watkins]] <ref> [[Chris Watkins]], [[Peter Dayan]] ('''1992'''). ''[http://www.gatsby.ucl.ac.uk/~dayan/papers/wd92.html Q-learning]''. [https://en.wikipedia.org/wiki/Machine_Learning_(journal) Machine Learning], Vol. 8, No. 2</ref>,