Mood as Representation of Momentum

Abstract

Experiences affect mood, which in turn affects subsequent experiences. Recent studies suggest two specific principles. First, mood depends on how recent reward outcomes differ from expectations. Second, mood biases the way we perceive outcomes (e.g., rewards), and this bias affects learning about those outcomes. We propose that this two-way interaction serves to mitigate inefficiencies in the application of reinforcement learning to real-world problems. Specifically, we propose that mood represents the overall momentum of recent outcomes, and its biasing influence on the perception of outcomes ‘corrects’ learning to account for environmental dependencies. We describe potential dysfunctions of this adaptive mechanism that might contribute to the symptoms of mood disorders.

Trends

With increasing use of computational models to understand human behavior, scientists have begun to model the dynamics of subjective states such as mood.

Recent data suggest that mood reflects the cumulative impact of differences between reward outcomes and expectations.

Behavioral and neural findings suggest that mood biases the perception of reward outcomes such that outcomes are perceived as better when one is in a good mood relative to when one is in a bad mood.

These two lines of research establish a bidirectional interaction between mood and reinforcement learning, which may play an important adaptive role in healthy behavior, and whose dysfunction might contribute to psychiatric disorders.