Emotions, emotion concepts, and the predictive mind

Abstract

This dissertation develops a constructionist account of emotion within the predictive processing framework. It argues that emotions are not the outputs of dedicated mechanisms, but are conceptually mediated forms of predictive inference. On this view, emotion concepts are understood as hierarchically organized generative models that guide the interpretation of bodily and environmental signals, structure patterns of regulation, and coordinate action. The dissertation advances this account in three ways. First, it argues that emotions represent organism–environment relations in an evaluative sense, capturing how situations matter for the organism's ongoing activity. Second, it develops an account of anxiety as a form of stalled inference under conditions of unresolved uncertainty, explaining its anticipatory and persistent character. Third, it shows that emotions can be attributed to nonhuman animals without requiring human-level conceptual sophistication, by understanding emotion concepts as embodied and graded predictive models. Taken together, the dissertation provides a unified account of emotion as part of a predictive, conceptually structured system that enables organisms to navigate a complex and uncertain world.