• 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

Daniel Bennett – A model of mood as integrated advantage