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.

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Biases in processing ambiguous emotional stimuli are thought to precede, and confer risk for, clinical depression. It has long been theorized that those who appraise ambiguous stimuli more negatively experience greater negative and less positive emotion in their day-to-day life, which in turn relates to symptoms of depression. To test this theory, we assessed links between a negativity bias task, ecological momentary assessments of emotion, and self-reported symptoms of depression in 154 young adults. Here, negativity bias was operationalized as the degree to which ambiguous emotional faces were rated negatively. We found that greater negativity bias in the laboratory predicted greater daily mean negative affect and lower positive affect across the semester. Critically, we also found that mean daily affect linked negativity bias with symptoms of depression. These results highlight how individual differences in information processing may be connected to real-world emotional profiles and risk for psychopathology.

Cross-species research suggests that exploratory behaviors increase during adolescence and relate to the social, affective, and risky behaviors characteristic of this developmental stage. However, how these typical adolescent behaviors manifest and relate in real-world settings remains unclear. Using geolocation tracking to quantify exploration—variability in daily movement patterns—over a 3-month period in 58 adolescents and adults (ages 13–27) in New York City, we investigated whether daily exploration varied with age and whether exploration related to social connectivity, risk taking, and momentary positive affect. In our cross-sectional sample, we found an association between daily exploration and age, with individuals near the transition to legal adulthood exhibiting the highest exploration levels. Days of higher exploration were associated with greater positive affect irrespective of age. Higher mean exploration was associated with greater social connectivity in all participants but was linked to higher risk taking selectively among adolescents. Our results highlight the interplay of exploration and socioemotional behaviors across development and suggest that societal norms may modulate their expression in naturalistic contexts.

Organisms learn from prediction errors (PEs) to predict the future. Laboratory studies using small financial outcomes find that humans use PEs to update expectations and link individual differences in PE-based learning to internalizing disorders. Because of the low-stakes outcomes in most tasks, it is unclear whether PE learning emerges in naturalistic, high-stakes contexts and whether individual differences in PE learning predict psychopathology risk. Using experience sampling to assess 625 college students’ expected exam grades, we found evidence of PE-based learning and a general tendency to discount negative PEs, an “optimism bias.” However, individuals with elevated negative emotionality, a personality trait linked to the development of anxiety disorders, displayed a global pessimism and learning differences that impeded accurate expectations and predicted future anxiety symptoms. A sensitivity to PEs combined with an aversion to negative PEs may result in a pessimistic and inaccurate model of the world, leading to anxiety.

Negative interpretation bias, the tendency to appraise ambiguous stimuli as threatening, shapes our emotional lives. Various laboratory tasks, which differ in stimuli features and task procedures, can quantify negative interpretation bias. However, it is unknown whether these tasks globally predict individual differences in real-world negative (NA) and positive (PA) affect. Across two studies, we tested whether different lab-based negative interpretation bias tasks predict daily NA and PA, measured via mobile phone across months. To quantify negative interpretation bias, Study 1 (N = 69) used a verbal, self-referential task whereas Study 2 (N = 110) used a perceptual, emotional image task with faces and scenes. Across tasks, negative interpretation bias was linked to heightened daily NA. However, only negative interpretation bias in response to ambiguous faces was related to decreased daily PA. These results illustrate the ecological validity of negative interpretation bias tasks and highlight converging and unique relationships between distinct tasks and naturalistic emotion.

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