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.
Highlights
With increasing use of computational models to understand human behavior, scientists have begun to model the dynamics of subjective states such as mood
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
Summary
Experiences affect mood, which in turn affects subsequent experiences. Recent studies suggest two specific principles. We argue that moods serve an important role in adaptive behavior, even in the modern world We elucidate this role by considering recent findings regarding the dynamics of mood, as well as its interaction with the processes of learning and decision making. 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. A recent study demonstrated that reported happiness during value-based decision making depends on reward expectations and how actual outcomes differ from these expectations [26]. These results suggest that happiness reflects a running average of recent RPEs in which different types of prediction errors may be differently weighted
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