Beyond the average: The role of variable reward sensitivity in eating disorders

Abstract

Eating disorders are often characterized by episodes of overeating and undereating. To date, most theories have explained the liability for such episodes by differences in traits such as reward sensitivity or cognitive control. Here, we review the evidence for a more parsimonious account of the waxing and waning in food intake by linking it to state-like variability of alleged traits such as reward sensitivity. To formally demonstrate that our variability model of eating disorders could explain a wide range of observed reward-related behavior, we conducted simulations of value-based choices and learning. These simulations based on well-established computational models of reinforcement learning and Bayesian sequential updating show how variability may arise and manifest in eating behavior. We argue that by reconceptualizing stable traits as distributions over likely states promoting adaptation, our proposed model integrates disparate findings and leads to novel predictions in a quantitative framework. Collectively, these emerging results call for a stronger emphasis on within-person variability to improve mechanistic insights into eating disorders.