Age Differences in Everyday Emotional Experience: Testing Core Predictions of Socioemotional Selectivity Theory With the MIVA Model

Objectives Emotional aging research is dominated by the idea of age-related improvements that result from shifts in motivation. Socioemotional selectivity theory (SST) proposes that as individuals age, they increasingly favor emotion-related goals and savor positive but avoid negative emotions. Previous age-comparative studies on everyday emotional experience typically were descriptive or studied the processes underlying emotional experience in isolation. We aimed at a more holistic approach to test hypotheses derived from SST regarding age-related differences in general emotional dispositions (i.e., anchoring), emotional reactivity, and emotion regulation by using a computational approach. Methods We applied our Model of Intraindividual Variability in Affect (MIVA) to data on everyday emotional experiences in an age-diverse sample (N = 378, age range 14-86 years). Parameter estimations were carried out within a Bayesian framework. Results Our results provide partial support for predictions derived from SST. Unexpectedly, anchoring showed a negative age trend, indicating a more positive affect disposition in younger, not older adults. Reactions to pleasant events showed no age trend. Reactivity to unpleasant events was highest in midlife and lower for younger and older adults. Consistent with SST, affect elicited by pleasant events was regulated less strongly by older adults and affect elicited by unpleasant events more strongly. Discussion As our results provide only partial support for SST, we revisit the phenomena that are at its foundations and provide suggestions on how to refine the theory.

Automated summary

Emotional aging research focuses on age-related improvements in motivation. Socioemotional selectivity theory proposes that as individuals age, they favor emotion-related goals and savor positive but avoid negative emotions. Our computational approach provides partial support for these predictions.