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The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. 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 model of emotional valence. This formulation asserts that agents infer their valence state based on the expected precision of their action model—an internal estimate of overall model fitness (“subjective fitness”). This index of subjective fitness can be estimated within any environment and exploits the domain generality of second-order beliefs (beliefs about beliefs). We show how maintaining internal valence representations allows the ensuing affective agent to optimize confidence in action selection preemptively. Valence representations can in turn be optimized by leveraging the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). AC tracks changes in fitness estimates and lends a sign to otherwise unsigned divergences between predictions and outcomes. We simulate the resulting affective inference by subjecting an in silico affective agent to a T-maze paradigm requiring context learning, followed by context reversal. This formulation of affective inference offers a principled account of the link between affect, (mental) action, and implicit metacognition. It characterizes how a deep biological system can infer its affective state and reduce uncertainty about such inferences through internal action (i.e., top-down modulation of priors that underwrite confidence). Thus, we demonstrate the potential of active inference to provide a formal and computationally tractable account of affect. Our demonstration of the face validity and potential utility of this formulation represents the first step within a larger research program. Next, this model can be leveraged to test the hypothesized role of valence by fitting the model to behavioral and neuronal responses.

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Emotional experience (EE) and trait emotional awareness (tEA) have recently become topics of considerable experimental/theoretical interest within the cognitive and neural sciences. However, to date there has been limited empirical focus on how individual differences in the factors contributing to EE (a state-based construct) might account for differences in tEA. To promote clear, well-guided empirical research in this area, in this article we first offer a concise review of the primary factors contributing to EE. We then provide a theoretical investigation into how individual differences in these factors (i.e., differences in affective response generation, affective response representation, and conscious access) could mechanistically account for differences in tEA; we also discuss plausible origins of these individual differences in light of current empirical findings. Finally, we outline possible experiments that would support (or fail to support) the role of each factor in explaining differences in tEA-and how this added knowledge could shed light on the known link between low tEA and multiple emotion-related mental and systemic medical disorders. (PsycINFO Database Record