Although the importance of emotions to self-regulation has been noted in the extant literature, little empirical research has examined how emotions are related to performance in complex skill learning. Using existing data of videogame playing, I first examined the incremental prediction of discrete emotions above general dimensions of positive and negative affect. I found that discrete emotions provided incremental prediction above general dimensions of affect, but that this was clearest and most consistent for positive activating emotions. These results suggest that emphasizing specific emotions may be more useful than generally focusing on negative or positive emotions in emotion control interventions. In Study 2, I conducted a laboratory study involving undergraduate males playing the same videogame as in Study 1. I examined two emotion control strategies, one targeting positive activating emotions (i.e., enthusiastic, excited, happy) and the other targeting positive deactivating emotions (i.e., calm, relaxed, at ease) in comparison to a no emotion control strategy group (i.e., the control condition). Using discontinuous growth modeling that distinguishes acquisition and adaptive performance, quantitative analyses showed that the strategy targeting positive deactivating emotions improved performance across acquisition and adaptation. Individuals in the positive deactivating and no emotion control group performed similarly. Additionally, the emotion control strategies did not increase the respective emotion scores. Qualitative analyses showed that individuals in the positive deactivating condition mentioned feeling calm, relaxed, and at ease was useful for reducing negative emotions and improving cognition and focus, both which likely improved performance. Results are discussed in regards to the importance of tailoring emotion control strategies to the performance context.