Search for: All records

Abstract The brain is organized into intrinsically connected functional networks that can be reliably identified during resting-state functional magnetic resonance imaging (fMRI). Healthy aging is marked by decreased network segregation, which is linked to worse cognitive functioning, but aging-related changes in emotion are less well characterized. Valence bias, which represents the tendency to interpret emotionally ambiguous information as positive or negative, is more positive in older than younger adults and is associated with differences in task-based fMRI activation in the amygdala, prefrontal cortex, and a cingulo-opercular (CO) network. Here, we examined valence bias, age, and resting-state network segregation of 12 brain networks in a sample of 221 healthy individuals from 6 to 80 years old. Resting-state network segregation decreased linearly with increasing age, extending prior reports of de-differentiation across the lifespan. Critically, a more positive valence bias was related to lower segregation of the default mode network (DMN), due to stronger functional connectivity of the DMN with CO and, to a lesser extent, the ventral attention network (VAN) in all participants. In contrast to this overall segregation effect, in participants over 39 years old (who tend to show a positive valence bias), bias was also related to weaker connectivity between the DMN and Reward networks. The present findings indicate that specific interactions between the DMN, a task control network (CO), an emotion processing network (Reward), and, to a weaker extent, an attention network (VAN), support a more positive valence bias, perhaps through regulatory control of self-referential processing and reduced emotional reactivity in aging. The current work offers further insight into the functional brain network alterations that may contribute to affective well-being and dysfunction across the lifespan. 

Individuals exhibit a systematic valence bias—a specific form of interpretation bias—in response to emo- tional ambiguity. Accumulating evidence suggests most people initially respond to emotional ambiguity negatively and differ only in subsequent responses. We hypothesized that trait-level cognitive reap- praisal—an emotion regulation strategy involving the reinterpretation of affective meaning of stimuli— might explain individual differences in valence bias. To answer this question, we conducted a random- effects meta-analysis of 14 effect sizes from 13 prior studies (n = 2,086), identified via Google Scholar searches. We excluded studies (a) in languages other than English, (b) from non-peer-reviewed sources, or (c) nonempirical sources. We included studies with (a) the Emotion Regulation Questionnaire, (b) a puta- tive measure of valence bias prior to any study-specific manipulations, and (c) adult human participants (i.e., 17+). Supporting our prediction, we found individuals with higher trait reappraisal exhibited a less negative bias (r = −.18, z = −4.04, p , .001), whereas there was a smaller, opposite effect for trait expressive sup- pression (r = .10, z = 2.14, p = .03). The effects did, however, vary across tasks with stronger effects observed among studies using the scrambled sentences task compared to the valence bias task. Although trait reappraisal accounted for only a small amount of variance, reappraisal may be one mechanism contrib- uting to variability in response to ambiguity. 

Ambiguous stimuli are useful for assessing emotional bias. For example, surprised faces could convey a positive or negative meaning, and the degree to which an individual interprets these expressions as positive or negative represents their “valence bias.” Currently, the most well-validated ambiguous stimuli for assessing valence bias include nonverbal signals (faces and scenes), overlooking an inherent ambiguity in verbal signals. This study identified 32 words with dual-valence ambiguity (i.e., relatively high intersubject variability in valence ratings and relatively slow response times) and length-matched clearly valenced words (16 positive, 16 negative). Preregistered analyses demonstrated that the words-based valence bias correlated with the bias for faces, r s (213) = .27, p < .001, and scenes, r s (204) = .46, p < .001. That is, the same people who interpret ambiguous faces/scenes as positive also interpret ambiguous words as positive. These findings provide a novel tool for measuring valence bias and greater generalizability, resulting in a more robust measure of this bias.