Craving of unhealthy food is a common target of self-regulation, but the neural systems underlying this process are understudied. In this study, participants used cognitive reappraisal to regulate their desire to consume idiosyncratically craved or not craved energy-dense foods, and neural activity during regulation was compared with each other and with the activity during passive viewing of energy-dense foods. Regulation of both food types elicited activation in classic top-down self-regulation regions including the dorsolateral prefrontal, inferior frontal, and dorsal anterior cingulate cortices. This main effect of regulation was qualified by an interaction, such that activation in these regions was significantly greater during reappraisal of craved (versus not craved) foods and several regions, including the dorsolateral prefrontal, inferior frontal, medial frontal, and dorsal anterior cingulate cortices, were uniquely active during regulation of personally craved foods. Body mass index significantly negatively correlated with regulation-related activation in the right dorsolateral PFC, thalamus, and bilateral dorsal ACC and with activity in nucleus accumbens during passive viewing of craved (vs. neutral, low-energy density) foods. These results suggest that several of the brain regions involved in the self-regulation of food craving are similar to other kinds of affective self-regulation and that others are sensitive to the self-relevance of the regulation target.

Objective

Research on eating relies on various indices (e.g., stable, momentary, neural) to accurately reflect food-related reactivity (e.g., disinhibition) and regulation (e.g., restraint) outside the laboratory. The degree to which they differentially predict real-world consumption remains unclear. Further, the predictive validity of these indices might vary depending on whether an individual is actively restricting intake.

Methods

We assessed food craving reactivity and regulation in 46 healthy participants (30 women, 18-30 years) using standard measurements in three modalities: a) self-reported (stable) traits using surveys popular in the eating literature, and b) momentary craving ratings and c) neural activation using aggregated functional magnetic resonance imaging data gathered during a food reactivity-and-regulation task. We then used these data to predict variance in real-world consumption of craved energy-dense "target" foods across 2 weeks among normal-weight participants randomly assigned to restrict or monitor target food intake.

Results

The predictive validity of four indices varied significantly by restriction. When participants were not restricting intake, momentary (B = 0.21, standard error [SE] = 0.05) and neural (B = 0.08, SE = 0.04) reactivity positively predicted consumption, and stable (B = -0.22, SE = 0.05) and momentary (B = -0.24, SE = 0.05) regulation negatively predicted consumption. When restricting, stable (B = 0.36, SE = 0.12) and neural (B = 0.51, SE = 0.12) regulation positively predicted consumption.

Conclusions

Commonly-used indices of regulation and reactivity differentially relate to an ecologically-valid eating measurement, depending on the presence of restriction goals, and thus have strong implications for predicting real-world behaviors.

Exposure to adversity (e.g., poverty, bereavement) is a robust predictor of disruptions in psychological functioning. However, people vary greatly in their responses to adversity; some experience severe long-term disruptions, others experience minimal disruptions or even improvements. We refer to the latter outcomes-faring better than expected given adversity-as psychological resilience. Understanding what processes explain resilience has critical theoretical and practical implications. Yet, psychology's understanding of resilience is incomplete, for two reasons: (a) We lack conceptual clarity, and (b) two major approaches to resilience-the stress and coping approach and the emotion and emotion-regulation approach-have limitations and are relatively isolated from one another. To address these two obstacles,we first discuss conceptual questions about resilience. Next, we offer an integrative affect-regulation framework that capitalizes on complementary strengths of both approaches. This framework advances our understanding of resilience by integrating existing findings, highlighting gaps in knowledge, and guiding future research.