Positive affect as a computational mechanism

Recent advances in the computational neuroscience of reward learning have produced a new perspective on happiness as an inference mechanism. According to this perspective, happiness serves to signal an increase in the overall availability of reward in one's environment, and helps adjust expectations accordingly. Here we discuss how this normative perspective on happiness can help ground other key concepts within the realm of positive affect which to date have lacked precise definitions. In particular, we propose a distinction between happiness as an emotion and happiness as a mood. Then, we define the respective roles of happiness and pleasure and explain how each contributes to anticipatory and consummatory affective responses. Finally, we examine how different types of positive affect might reflect inferences about different types of reward and punishment. The implications and proposals highlighted offer fertile grounds for future research into the function and dynamics of positive affect.