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A treatment of emotion as a means of meta-optimisation in cognitive systems is presented, drawing upon research in neuroscience and reinforcement learning. In particular, emotion is motivated and explained against the background of the freeenergy principle and the Bayesian brain hypothesis, from the perspective of appraisal theory. Various implications of these models are examined in the context of reinforcement learning through a review of recent research. Based on the informationprocessing view of computation, a probabilistic approach to modelling computational systems is tentatively proposed in order to better handle the sort of probabilistic information processing involved in modelling cognition. By taking information gain to be the essential property of computation, it is suggested that a general computing system may be modelled as updates of parameters defining probability distributions.