Fernandez Velasco, P., Loev, S. Affective experience in the predictive mind: a review and new integrative account. Synthese (2020). https://doi.org/10.1007/s11229-020-02755-4
Are you interested in emotions? Are you interested in a grand unifying theory of the mind? Or, perhaps, you’re interested in both? If so then you might find this article interesting. One of its contributions is a review of emotion theories in the predictive processing framework. Another is the development of a new emotion theory in the predictive processing framework.
For some more info, here is the abstract:
This paper aims to offer an account of affective experiences within Predictive Processing, a novel framework that considers the brain to be a dynamical, hierarchical, Bayesian hypothesis-testing mechanism. We begin by outlining a set of common features of affective experiences (or feelings) that a PP-theory should aim to explain: feelings are conscious, they have valence, they motivate behaviour, and they are intentional states with particular and formal objects. We then review existing theories of affective experiences within Predictive Processing and delineate two families of theories: Interoceptive Inference Theories (which state that feelings are determined by interoceptive predictions) and Error Dynamics Theories (which state that feelings are determined by properties of error dynamics). We highlight the strengths and shortcomings of each family of theories and develop a synthesis: the Affective Inference Theory. Affective Inference Theory claims that valence corresponds to the expected rate of prediction error reduction. In turn, the particular object of a feeling is the object predicted to be the most likely cause of expected changes in prediction error rate, and the formal object of a feeling is a predictive model of the expected changes in prediction error rate caused by a given particular object. Finally, our theory shows how affective experiences bias action selection, directing the organism towards allostasis and towards optimal levels of uncertainty in order to minimise prediction error over time.
