Mood and anxiety disorders involve recurring, maladaptive patterns of distinct emotions and moods. Here, we argue that understanding these maladaptive patterns first requires understanding how emotions and moods guide adaptive behavior. We thus review recent progress in computational accounts of emotion that aims to explain the adaptive role of distinct emotions and mood. We then highlight how this emerging approach could be used to explain maladaptive emotions in various psychopathologies. In particular, we identify three computational factors that may be responsible for excessive emotions and moods of different types: self-intensifying affective biases, misestimations of predictability, and misestimations of controllability. Finally, we outline how the psychopathological roles of these factors can be tested, and how they may be used to improve psychotherapeutic and psychopharmacological interventions.
Bibliographical noteFunding Information:
This work has been made possible by NIH grants R01MH124092 and R01MH125564, ISF grant 1094/20, and US-Israel BSF grant 2019801.
© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
- Computational modeling
- Reinforcement learning