
A computational and neural model of momentary subjective well-being
Description
**Faculty Candidate Search - Cognitive Neuroscience**
The subjective well-being or happiness of individuals is an important metric for societies, but we know little about how the cumulative influence of daily life events are aggregated into subjective feelings. Using computational modeling, I show that momentary happiness in a decision-making task is explained not by task earnings, but by the combined influence of past rewards and expectations. The robustness of this account was evident in a large-scale smartphone-based replication. I use a combination of neuroimaging and pharmacology to investigate the neural basis of mood dynamics, finding that it relates to dopamine. I then show that this computational approach can be used to investigate the link between mood and behavior and the dynamics of mood in psychiatric disorders like depression.