摘要：Negativity bias is not only central to mood and anxiety disorders, but can powerfully impact our decision-making across domains (e.g., financial, medical, social). This project builds on previous work examining negativity bias using dual-valence ambiguity. Specifically, although some facial expressions have a relatively clear negative (angry) or positive valence (happy), surprised expressions are interpreted negatively by some and positively by others, providing insight into one's valence bias. Here, we examine putative sources of variability that distinguish individuals with a more negative versus positive valence bias using structural equation modeling. Our model reveals that one's propensity toward negativity (operationalized as temperamental negative affect and internalizing symptomology) predicts valence bias particularly in older adulthood when a more positive bias is generally expected. Further, variability in social connectedness (a propensity to seek out social connections, use those connections to regulate one's own emotions, and be empathic) emerges as a notable and unique predictor of valence bias, likely because these traits help to override an initial, default negativity. We argue that this task represents an important approach to examining variability in affective bias, and can be specifically useful across the lifespan and in populations with internalizing disorders or even subclinical symptomology.
其他摘要：Abstract Negativity bias is not only central to mood and anxiety disorders, but can powerfully impact our decision-making across domains (e.g., financial, medical, social). This project builds on previous work examining negativity bias using dual-valence ambiguity. Specifically, although some facial expressions have a relatively clear negative (angry) or positive valence (happy), surprised expressions are interpreted negatively by some and positively by others, providing insight into one’s valence bias. Here, we examine putative sources of variability that distinguish individuals with a more negative versus positive valence bias using structural equation modeling. Our model reveals that one’s propensity toward negativity (operationalized as temperamental negative affect and internalizing symptomology) predicts valence bias particularly in older adulthood when a more positive bias is generally expected. Further, variability in social connectedness (a propensity to seek out social connections, use those connections to regulate one’s own emotions, and be empathic) emerges as a notable and unique predictor of valence bias, likely because these traits help to override an initial, default negativity. We argue that this task represents an important approach to examining variability in affective bias, and can be specifically useful across the lifespan and in populations with internalizing disorders or even subclinical symptomology.
