Food reinforcement architecture: A framework for impulsive and compulsive overeating and food abuse.
Obesity (Silver Spring)
; 31(7): 1734-1744, 2023 07.
Article
de En
| MEDLINE
| ID: mdl-37368515
ABSTRACT
Few 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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Hyperphagie
/
Comportement alimentaire
Type d'étude:
Prognostic_studies
Limites:
Humans
Langue:
En
Journal:
Obesity (Silver Spring)
Sujet du journal:
CIENCIAS DA NUTRICAO
/
FISIOLOGIA
/
METABOLISMO
Année:
2023
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique