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.

Kato, A., Shimomura, K., Ognibene, D., Parvaz, M., Berner, L., Morita, K., et al. (2023). Computational models of behavioral addictions: State of the art and future directions. ADDICTIVE BEHAVIORS, 140(May 2023) [10.1016/j.addbeh.2022.107595].

Computational models of behavioral addictions: State of the art and future directions

Ognibene D.;
2023

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.
Articolo in rivista - Articolo scientifico
Active inference; Bayesian; Computational modelling; Model-based; Model-free; Neural models; Neural simulations; Reinforcement learning;
English
22-dic-2022
2023
140
May 2023
107595
partially_open
Kato, A., Shimomura, K., Ognibene, D., Parvaz, M., Berner, L., Morita, K., et al. (2023). Computational models of behavioral addictions: State of the art and future directions. ADDICTIVE BEHAVIORS, 140(May 2023) [10.1016/j.addbeh.2022.107595].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/402899
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