Temporal Dynamics of Real-World Emotion Are More Strongly Linked to Prediction Error Than Outcome

Primarily based on laboratory studies, theories of affect propose that emotions are driven by the valence of outcomes as well as the difference between the outcome itself and the expected outcome (i.e., the prediction error [PE]). Yet no work has assessed the drivers of emotion using real-world, personally meaningful events on timescales over which human emotion unfolds. We developed an event-triggered, ecological momentary assessment procedure measuring positive and negative affect (PA and NA, respectively) in university students as they received exam grades for which they had made predictions. We split data into exploratory and confirmatory samples, and built computational models predicting the time course of PA and NA and demonstrate that a model incorporating both exam grade and grade PE accounted for the time course of PA and NA better than a model solely using exam grades. Further, grade PEs were stronger predictors of the time course of PA and NA than the grades themselves. Similarly, the effects of PEs also persisted longer for NA than PA. These data indicate that deviations from expectations are critical determinants of the temporal dynamics of real-world emotion.