A cognitive-computational account of mood swings in adolescence

Teenagers have a reputation for being fickle, both in their choices and in their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here we consider adolescents’ mood swings from a novel computational perspective, grounded in Reinforcement Learning. This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that, in a bidirectional manner, mood biases learning and choice. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents’ mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.