Food reinforcement architecture: A framework for impulsive and compulsive overeating and food abuse

Abstract

AbstractFew reward‐based theories address key drivers of susceptibility to food cues and consumption beyond fullness. Decision‐making and habit formation are governed by reinforcement‐based learning processes that, when overstimulated, can drive unregulated hedonically motivated overeating. Here, a model food reinforcement architecture is proposed that uses fundamental concepts in reinforcement and decision‐making to identify maladaptive eating habits that can lead to obesity. This model is unique in that it identifies metabolic drivers of reward and incorporates neuroscience, computational decision‐making, and psychology to map overeating and obesity. Food reinforcement architecture identifies two paths to overeating: a propensity for hedonic targeting of food cues contributing to impulsive overeating and lack of satiation that contributes to compulsive overeating. A combination of those paths will result in a conscious and subconscious drive to overeat independent of negative consequences, leading to food abuse and/or obesity. Use of this model to identify aberrant reinforcement learning processes and decision‐making systems that can serve as markers of overeating risk may provide an opportunity for early intervention in obesity.