Explore the vast range of titles available.

Previous theorizing suggests there are multiple means by which people regulate their emotions and impulses, but that these strategies vary in the degree to which they support goal attainment. Some have proposed that proactive strategies (e.g. situation selection, distraction) may be particularly effective, while interventive strategies (e.g. suppression) are less effective. Despite these diverging predictions, researchers have yet to examine spontaneous use of these strategies and their respective and combined efficacy when applied to momentary food desires experienced in daily life. In the present study, we assessed eating patterns for one week via ecological momentary assessment in college-aged women (N = 106). Results from pre-registered analyses indicated that using a variety of strategies, including preventative strategies such as situation selection and distraction, was associated with greater self-control success, as indexed by weaker desires, higher resistance, lower likelihood of enacting desires, and less food consumed. A similar pattern was observed when participants implemented additional strategies during desire episodes, which they were more likely to do when their desires conflicted with other self-regulatory goals. All associations were observed while controlling for momentary hunger levels, dieting status, age, and body mass index. These findings are consistent with a growing body of work assessing people’s spontaneous use of emotion regulation strategies in everyday contexts, suggesting potential meta-motivational tendencies marked by flexible and adaptive use of self-regulatory strategies.

As a form of attention, mindfulness is qualitatively receptive and non-reactive, and is thought to facilitate adaptive emotional responding. One suggested mechanism is that mindfulness facilitates disengagement from an affective stimulus and thereby decreases affective reactivity. However, mindfulness has been conceptualized as a state, intervention, and trait. Because evidence is mixed as to whether self-reported trait mindfulness decreases affective reactivity, we used a multi-method approach to study the relationship between individual differences in self-reported trait mindfulness and electrocortical, electrodermal, electromyographic, and self-reported responses to emotional pictures. Specifically, while participants (N = 51) passively viewed pleasant, neutral, and unpleasant IAPS pictures, we recorded high-density (128 channels) electrocortical, electrodermal, and electromyographic data to the pictures as well as to acoustic startle probes presented during the pictures. Afterwards, participants rated their subjective valence and arousal while viewing the pictures again. If trait mindfulness spontaneously reduces general emotional reactivity, then for individuals reporting high rather than low mindfulness, response differences between emotional and neutral pictures would show relatively decreased early posterior negativity (EPN) and late positive potential (LPP) amplitudes, decreased skin conductance responses, and decreased subjective ratings for valence and arousal. High mindfulness would also be associated with decreased emotional modulation of startle eyeblink and P3 amplitudes. Although results showed clear effects of emotion on the dependent measures, in general, mindfulness did not moderate these effects. For most measures, effect sizes were small with rather narrow confidence intervals. These data do not support the hypothesis that individual differences in self-reported trait mindfulness are related to spontaneous emotional responses during picture viewing.

Conversations shape future behaviors, particularly among young adults. However, young adults vary widely in their susceptibility to peer influence. What neural processes relate to this susceptibility? We examined whether activity in brain regions associated with social rewards and making sense of others’ minds relates to a common health behavior—drinking, following conversations about alcohol. We studied ten social groups of college students ( N  = 104 students; 4760 total observations) across two university campuses. We collected whole-brain fMRI data while participants viewed photographs of peers with whom they tended to drink at varying frequencies. Next, using ecological momentary assessment, we tracked alcohol conversations and drinking twice daily for 28 days. On average, talking about alcohol was associated with a higher likelihood of next-day drinking. Controlling for baseline drinking, participants who responded more strongly to peers with whom they drank alcohol more frequently—in brain regions associated with social rewards and mentalizing—showed a stronger, positive association between alcohol conversations and next-day drinking. Conversely, stronger neural responses to peers with whom they drank less frequently decoupled associations between alcohol conversations and next-day drinking. We conceptually replicate prior findings linking conversations and drinking in an observational, longitudinal setting and provide new evidence that neural responses to peers moderate links between alcohol conversations and drinking behavior among young adults.

Modifying behaviors, such as alcohol consumption, is difficult. Creating psychological distance between unhealthy triggers and one’s present experience can encourage change. Using two multisite, randomized experiments, we examine whether theory-driven strategies to create psychological distance—mindfulness and perspective-taking—can change drinking behaviors among young adults without alcohol dependence via a 28-day smartphone intervention (Study 1, N  = 108 participants, 5492 observations; Study 2, N  = 218 participants, 9994 observations). Study 2 presents a close replication with a fully remote delivery during the COVID-19 pandemic. During weeks when they received twice-a-day intervention reminders, individuals in the distancing interventions reported drinking less frequently than on control weeks—directionally in Study 1, and significantly in Study 2. Intervention reminders reduced drinking frequency but did not impact amount. We find that smartphone-based mindfulness and perspective-taking interventions, aimed to create psychological distance, can change behavior. This approach requires repeated reminders, which can be delivered via smartphones.

Evidence on the harms and benefits of social media use is mixed, in part because the effects of social media on well-being depend on a variety of individual difference moderators. Here, we explored potential neural moderators of the link between time spent on social media and subsequent negative affect. We specifically focused on the strength of correlation among brain regions within the frontoparietal system, previously associated with the top-down cognitive control of attention and emotion. Participants (N = 54) underwent a resting state functional magnetic resonance imaging scan. Participants then completed 28 days of ecological momentary assessment and answered questions about social media use and negative affect, twice a day. Participants who spent more than their typical amount of time on social media since the previous time point reported feeling more negative at the present moment. This within-person temporal association between social media use and negative affect was mainly driven by individuals with lower resting state functional connectivity within the frontoparietal system. By contrast, time spent on social media did not predict subsequent affect for individuals with higher frontoparietal functional connectivity. Our results highlight the moderating role of individual functional neural connectivity in the relationship between social media and affect.

Neural patterns associated with viewing energy-dense foods can predict changes in eating-related outcomes. However, most research on this topic is limited to one follow-up time point, and single outcome measures. The present study seeks to add to that literature by employing a more refined assessment of food craving and consumption outcomes along with a more detailed neurobiological model of behavior change over several time points. Here, a community sample of 88 individuals (age: = 39.17, = 3.47; baseline BMI: = 31.5, = 3.9, range 24-42) with higher body mass index (BMI) performed a food craving reactivity and regulation task while undergoing functional magnetic resonance imaging. At that time-and 1, 3, and 6 months later-participants reported craving for and consumption of healthy and unhealthy foods via the Food Craving Inventory (FCI) and ASA24 (N at 6 months = 52-55 depending on the measure). hypotheses that brain activity associated with both viewing and regulating personally desired unhealthy, energy-dense foods would be associated with self-reported craving for and consumption of unhealthy foods at baseline were not supported by the data. Instead, regression models controlling for age, sex, and BMI demonstrated that brain activity across several regions measured while individuals were regulating their desires for unhealthy food was associated with the self-reported craving for and consumption of healthy food. The hypothesis that vmPFC activity would predict patterns of healthier eating was also not supported. Instead, linear mixed models controlling for baseline age and sex, as well as changes in BMI, revealed that more regulation-related activity in the dlPFC, dACC, IFG, and vmPFC at baseline predicted decreases in the craving for and consumption of healthy foods over the course of 6 months.

Considerable evidence points to a link between body mass index (BMI), eating behavior, and the brain's reward system. However, much of this research focuses on food cue reactivity without examining the subjective valuation process as a potential mechanism driving individual differences in BMI and eating behavior. The current pre-registered study ( https://osf.io/n4c95/ ) examined the relationship between BMI, healthy eating, and subjective valuation of healthy and unhealthy foods in a community sample of individuals with higher BMI who intended to eat more healthily. Particularly, we examined: (1) alterations in neurocognitive measures of subjective valuation related to BMI and healthy eating; (2) differences in the neurocognitive valuation for healthy and unhealthy foods and their relation to BMI and healthy eating; (3) and whether we could conceptually replicate prior findings demonstrating differences in neural reactivity to palatable vs. plain foods. To this end, we scanned 105 participants with BMIs ranging from 23 to 42 using fMRI during a willingness-to-pay task that quantifies trial-by-trial valuation of 30 healthy and 30 unhealthy food items. We measured out of lab eating behavior via the Automated Self-Administered 24 H Dietary Assessment Tool, which allowed us to calculate a Healthy Eating Index (HEI). We found that our sample exhibited robust, positive linear relationships between self-reported value and neural responses in regions previously implicated in studies of subjective value, suggesting an intact valuation system. However, we found no relationship between valuation and BMI nor HEI, with Bayes Factor indicating moderate evidence for a null relationship. Separating the food types revealed that healthy eating, as measured by the HEI, was inversely related to subjective valuation of unhealthy foods. Imaging data further revealed a stronger linkage between valuation of healthy (compared to unhealthy) foods and corresponding response in the ventromedial prefrontal cortex (vmPFC), and that the interaction between healthy and unhealthy food valuation in this region is related to HEI. Finally, our results did not replicate reactivity differences demonstrated in prior work, likely due to differences in the mapping between food healthiness and palatability. Together, our findings point to disruptions in the valuation of unhealthy foods in the vmPFC as a potential mechanism influencing healthy eating.

Digital interventions can change behaviors like alcohol use, but effectiveness varies widely across individuals. Accurately identifying non-responders—i.e., those least (vs. most) likely to change their behavior—before intervention delivery is difficult. Individual intervention effectiveness predictions from prior studies perform only slightly above chance (e.g., AUC ≈0.60; balanced accuracy ≈0.60). We present a novel approach integrating multimodal data across theory-driven domains—including psychological assessments, social network data, and neural responses to alcohol cues—to make ex-ante predictions about the effectiveness of smartphone-delivered alcohol interventions targeting psychological distancing in young adults (Study 1: N  = 67; Study 2: N  = 114). Demonstrating the feasibility of this approach, random forest models predicted individual differences in intervention effectiveness (Study 1: balanced accuracy = 0.71, 95% CI: 0.69–0.73, p  = .020; AUC = 0.87, 95% CI: 0.85–0.88, p  = .020) and replicated in a an external test sample (Study 2, balanced accuracy = 0.68; AUC = 0.68, 95% CI: 0.54–0.82), meeting clinical-utility thresholds from prior digital health studies (balanced accuracy = 0.67; correctly classifying (non)responders 67% of the time). Interventions were most effective for participants who perceived their peers as moderate but frequent drinkers. Peer drinking perceptions may serve as a low-burden indicator to support early identification of non-responders in preventive alcohol interventions among young adults. Future work can apply and extend the multimodal approach developed here for adaptive tailoring of digital behavior change interventions in real-world settings.

Exposure to food cues activates the brain's reward system and undermines efforts to regulate impulses to eat. During explicit regulation, lateral prefrontal cortex activates and modulates the activity in reward regions and decreases food cravings. However, the extent to which between-person differences in the recruitment of regions associated with reward processing, subjective valuation and regulation during food cue exposure-absent instructions to regulate-predict body composition and daily eating behaviors is unclear. In this preregistered study, we pooled data from five functional magnetic resonance imaging (fMRI) samples (N = 262) to examine whether regions associated with reward, valuation and regulation, as well as whole-brain pattern expression indexing these processes, were recruited during food cue exposure and associated with body composition and real-world eating behavior. Regression models for a single a priori analytic path indicated that univariate and multivariate measures of reward and valuation were associated with individual differences in body mass index and the enactment of daily food cravings. Specification curve analyses further revealed reliable associations between univariate and multivariate neural indicators of reactivity, regulation and valuation and all outcomes. These findings highlight the utility of these methods to elucidate brain-behavior associations and suggest that multiple processes are implicated in proximal and distal markers of eating behavior.

Abstract Exposure to food cues activates the brain’s reward system and undermines efforts to regulate impulses to eat. During explicit regulation, lateral prefrontal cortex activates and modulates the activity in reward regions and decreases food cravings. However, the extent to which between-person differences in the recruitment of regions associated with reward processing, subjective valuation and regulation during food cue exposure—absent instructions to regulate—predict body composition and daily eating behaviors is unclear. In this preregistered study, we pooled data from five functional magnetic resonance imaging (fMRI) samples (N = 262) to examine whether regions associated with reward, valuation and regulation, as well as whole-brain pattern expression indexing these processes, were recruited during food cue exposure and associated with body composition and real-world eating behavior. Regression models for a single a priori analytic path indicated that univariate and multivariate measures of reward and valuation were associated with individual differences in body mass index and the enactment of daily food cravings. Specification curve analyses further revealed reliable associations between univariate and multivariate neural indicators of reactivity, regulation and valuation and all outcomes. These findings highlight the utility of these methods to elucidate brain–behavior associations and suggest that multiple processes are implicated in proximal and distal markers of eating behavior.