Computational models of behavioral addictions: State of the art and future directions. (May 2023) - Kato, Ayaka; Shimomura, Kanji; Ognibene, Dimitri; Parvaz, Muhammad A.; Berner, Laura A.; Morita, Kenji; Fiore, Vincenzo G.
Highlights: Behavioral addictions share neurobiological and behavioral characteristics with SUDs. Computational modeling may help clarify underlying mechanisms. Aberrant model-based & -free control are presented as common computational principles. Reinforcement learning, Bayesian inference, and neural models are introduced. Possible extensions of models for SUDs, as well as new perspectives, are discussed. Abstract: Non-pharmacological behavioral addictions, such as pathological gambling, videogaming, social networking, or internet use, are becoming major public health concerns. It is not yet clear how behavioral addictions could share many major neurobiological and behavioral characteristics with substance use disorders, despite the absence of direct pharmacological influences. A deeper understanding of the neurocognitive mechanisms of addictive behavior is needed, and computational modeling could be one promising approach to explain intricately entwined cognitive and neural dynamics. This review describes computational models of addiction based on reinforcement learning algorithms, Bayesian inference, and biophysical neural simulations. We discuss whether computational frameworks originally conceived to explain maladaptive behavior in substance use disorders can be effectively extended to non-substance-related behavioral addictions. Moreover, we introduce recent studies on behavioral addictions that exemplify the possibility of such extension and propose future directions.