A Computational and Neural Model for Mood Dynamics
The happiness of individuals is an important metric for societies, but we know relatively little about how daily life events are aggregated into subjective feelings. We show that happiness depends on the history of expectations and prediction errors resulting from those expectations, a result we have now replicated in thousands of individuals using smartphone-based data collection (https://happinessquest.app). Using fMRI, we show how happiness relates to neural activity and to the neuromodulator dopamine. Using computational models for decision making and reinforcement learning, we show how feelings vary across individuals during multiple tasks and in relation to major depression.
Department of Psychology