- Thursday, January 21st 2021 at 21:00 - 22:00 UK (Other timezones)
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Mood exerts a pervasive effect on cognition and behavior and, at the same time, mood itself is thought to fluctuate slowly as a product of feedback from interactions with the environment. Here I present a new computational theory of the valence of mood—the Integrated Advantage model—that seeks to account for this bidirectional interaction. Adopting theoretical formalisms from reinforcement learning, I propose to conceptualize the valence of mood as a leaky integral of an agent’s appraisals of the Advantage of its actions. This model generalizes and extends previous models of mood wherein affective valence was conceptualized as a moving average of reward prediction errors. I will show how this model of mood provides a principled and parsimonious explanation for a number of contextual effects on mood from the affective science literature, including expectation- and surprise-related effects, counterfactual effects from information about foregone alternatives, action-typicality effects, and action/inaction asymmetry. I will conclude by considering the predictions of this model for mood disorders such as major depression and bipolar disorder.
Daniel Bennett, PhD
Research Fellow
Department of Psychiatry
Monash University