Controllability arbitrates between distinct emotions with opposing effects on behavior

Emotions consistently shape learning and decision-making, yet their study remains challenging because they are internal states that cannot be directly observed. Recent theory offers a way to overcome this challenge by mapping two classes of emotions onto distinct reinforcement-learning computations, with environmental controllability determining which computation dominates. In controllable settings, emotions guide actions, motivating greater investment of effort and other resources following disappointing outcomes to improve performance. In uncontrollable settings, emotions track reward availability, with disappointing outcomes suppressing reward-seeking behavior and good outcomes amplifying it. We tested this model in a treasure-hunt task (N=509) and found that controllability modulated emotional responses to prediction errors, which in turn determined changes in resource investment. Applying the framework to professional tennis matches (N=6,715) revealed parallel effects: performance changes reflected the same interaction between prediction errors and controllability. Thus, in the laboratory and the real world, controllability arbitrates between distinct emotional responses that shape adaptive and maladaptive behavior.