Loss of control over eating: A systematic review of task based research into impulsive and compulsive processes in binge eating

NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS

卷:129

Review

Waltmann, Maria^1,2,3  Herzog, Nadine^2,3  Horstmann, Annette^2,3,4  Deserno, Lorenz^1,2,3,5 

[1] Univ Wurzburg, Ctr Mental Hlth, Dept Child & Adolescent Psychiat, Psychosomat & Psychotherapy, Margarete Hoppel Pl 1, D-97080 Wurzburg, Germany

[2] Max Planck Inst Human Cognit & Brain Sci, Stephanstr 1, D-04103 Leipzig, Germany

[3] Univ Leipzig, Med Ctr, IFB Adipos Dis, Leipzig, Germany

[4] Univ Helsinki, Fac Med, Dept Psychol & Logoped, Helsinki, Finland

[5] Tech Univ Dresden, Dept Psychiat & Psychotherapy, Dresden, Germany

关键词: Binge eating;    Behavioral control;    Impulsivity;    Compulsivity;    Neurocognitive tasks;    Computational modeling;   

DOI  :  10.1016/j.neubiorev.2021.07.016

来源: Elsevier

【 摘 要 】

Recurring episodes of excessive food intake in binge eating disorder can be understood through the lens of behavioral control systems: patients repeat maladaptive behaviors against their explicit intent. Self-report measures show enhanced impulsivity and compulsivity in binge eating (BE) but are agnostic as to the processes that might lead to impulsive and compulsive behavior in the moment. Task-based neurocognitive investigations can tap into those processes. In this systematic review, we synthesize neurocognitive research on behavioral impulsivity and compulsivity in BE in humans and animals, published between 2010-2020. Findings on impulsivity are heterogeneous. Findings on compulsivity are sparse but comparatively consistent, indicating an imbalance of goal-directed and habitual control as well as deficits in reversal learning. We urge researchers to address heterogeneity related to mood states and the temporal dynamics of symptoms, to systematically differentiate contributions of body weight and BE, and to ascertain the validity and reliability of tasks. Moreover, we propose to further scrutinize the compulsivity findings to unravel the computational mechanisms of a potential reinforcement learning deficit.

【 授权许可】

Free