Teenagers have a reputation for being fickle, in both their choices and 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 (RL). 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 mood biases learning and choice in a bidirectional manner. 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.

中文翻译：

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青春期情绪波动的认知计算解释

众所周知，青少年的选择和情绪都变化无常。这种变化可能有助于青少年开始独立地适应新环境。然而，最近，青少年情绪低落也与精神病理学有关。在这里，我们以强化学习（RL）为基础，从一种新颖的计算角度来考虑青少年的情绪波动。该模型提出，情绪是由环境结果的惊喜以及我们从这些惊喜中学到的东西决定的。它还表明情绪以双向方式影响学习和选择。整合独立的研究路线，我们对青少年的情绪、学习和选择动态如何相互影响进行了认知计算解释，并对规范和心理病理学的发展产生了影响。