Flexible control in processing affective and non-affective material predicts individual differences in trait resilience

Abstract

Trait resilience is a stable personality characteristic that involves the self-reported ability to flexibly adapt to emotional events and situations. The present study examined cognitive processes that may explain individual differences in trait resilience. Participants completed self-report measures of trait resilience, cognitive flexibility and working memory capacity tasks, and a novel affective task-switching paradigm that assesses the ability to flexibly switch between processing the affective versus non-affective qualities of affective stimuli (i.e., flexible affective processing). As hypothesised, cognitive flexibility and flexible affective processing were unique predictors of trait resilience. Working memory capacity was not predictive of trait resilience, indicating that trait resilience is tied to specific cognitive processes rather than overall better cognitive functioning. Cognitive flexibility and flexible affective processing were not associated with other trait measures, suggesting that these flexibility processes are unique to trait resilience. This study was among the first to investigate the cognitive abilities underlying trait resilience.

Keywords:

• Resilience
• Cognitive flexibility
• Executive control
• Task-switching

Notes

¹This task is structurally similar to the extrinsic affective Simon task (Voss & Klauer, 2007).

²Given the relatively large number of excluded trials, we repeated all analyses with a response window of 4000 ms, leading to the exclusion of 4.8% of trials (approximately the same number of excluded trials as in the cognitive flexibility task).

³When using the 4000 ms response window, the main results were replicated, although the effects were somewhat weaker due to increased RT standard deviations. Inconsistent affective switches remained a significant predictor of trait resilience (p<.05) and cognitive flexibility was a marginally significant predictor (p=.06).