Deeply Felt Affect: The Emergence of Valence in Deep Active Inference

The positive-negative axis of emotional valence has long been recognised as fundamental to adaptive behaviour, but its domain-generality has largely eluded formal theories and modelling. Using deep active inference – a hierarchical inference scheme that rests on inverting a model of how sensory data are generated – we develop a principled Bayesian account of emotional valence. This formulation associates valence with subjective fitness and exploits the domain-generality of second-order beliefs (i.e., beliefs about beliefs). We construct an affective agent that infers its valence state from the expected precision of its phenotype-congruent action model (i.e., subjective fitness) in any given environment. The ensuing affective states then optimise that confidence pre-emptively. The evidence for inferred (i.e., ‘felt’) valenced states depends upon the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). We simulate affective inference in a T-maze paradigm requiring context learning, followed by context reversal. The result is a deep (biologically plausible) agent that infers its affective state and reduces its uncertainty through internal action (i.e., optimises prior beliefs that underwrite confidence). Thus, we demonstrate the potential of active inference in providing a formal and computationally tractable account of the link between affect, (mental) action, and implicit meta-cognition.