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Depression, like many psychiatric disorders, is a disorder of affect. Over the past decades, a large number of affective issues in depression have been characterised, both in human experiments and animal models of the dis order. Over the same period, experimental neuroscience, helped by com putational theories such as reinforcement learning, has provided detailed descriptions of the psychology and neurobiology of affective decision mak ing. Here, we attempt to harvest the advances in the understanding of the brain's normal dealings with rewards and punishments to dissect out and define more clearly the components that make up depression. We start by exploring changes to primary reinforcer sensitivity in the learned helpless ness animal models of depression. Then, a detailed formalisation of control in a goal-directed decision making framework is presented and related to animal and human data. Finally, we show how serotonin's joint involve ment in reporting negative values and inhibiting actions may explain some aspects of its involvement in depression. Throughout, aspects of depres sion are seen as emerging from normal affective function and reinforcement learning, and we thus conclude that computational descriptions of normal affective function provide one possible avenue by which to define an aetiol ogy of depression.