Psychobiological and neurocomputational investigations in addictive disorders have largely focused on the effects of substances of abuse on neural dynamics, cognitive processes and behavior (cf. reviews: Everitt and Robbins, 2016, Koob and Volkow, 2016, Mollick and Kober, 2020, Redish et al., 2008, Smith et al., 2021). However, recent studies strongly suggest that non-pharmacological behavioral addictions share with substance use disorders key neurobiological (Antons et al., 2020, Potenza, 2013), computational (Lindstrom et al., 2021, Ognibene et al., 2019, Redish et al., 2007, Shimomura et al., 2021), and behavioral features (Grant and Chamberlain, 2014, Grant et al., 2010). These include widely accepted behavioral addictions such as pathological gambling (el-Guebaly, Mudry, Zohar, Tavares, & Potenza, 2012), as well as others on which the consensus is still forming, such as videogaming (Petry and O'Brien, 2013, Yao et al., 2017), social network or internet addiction (Jorgenson et al., 2016, Veisani et al., 2020), compulsive buying (Granero et al., 2016, Grant et al., 2010), compulsive sexual behavior or pornography addiction (Griffiths, 2016, Love et al., 2015) and finally, more controversial, disordered eating behaviors such as binge eating (Wiss et al., 2020, Wiss et al., 2018, Wiss et al., 2017).

In this review, we cast a wide net relying on an inclusive definition of addictions: a relapsing, chronic disorder characterized by an initial pursuit of a desired outcome that leads to the inflexible repetition of maladaptive behaviors, despite the harmful consequences (Everitt and Robbins, 2016, Koob and Volkow, 2016). This definition highlights two complementary elements of behavioral and cognitive control in addictions. First, it emphasizes a transition from reinforcing action-outcome associations to compulsive stimulus-responses, i.e., from goal-oriented to habitual behavior (Ersche et al., 2016, Everitt and Robbins, 2013, Volkow and Morales, 2015). In other words, an ‘urge’ to respond to a reinforced cue is triggered irrespective of an actual desire for the outcome (cf. 'need' vs 'want', Berridge & Robinson, 2016) or any assessment about desired future environment or body states (cf. 'model-free control', Dolan & Dayan, 2013). Second, the chronic and relapsing elements of the definition assign an important role to an underperforming goal-oriented behavior and forward planning (or 'model-based control', cf. Dolan & Dayan, 2013), possibly due to an incomplete, incorrect, or otherwise impaired belief structure or internal model of both environment and body states. For instance, incorrect representations of future positive and negative interoceptive outcomes can lead to craving (Grimm et al., 2001, Gu and Filbey, 2017), often followed by the reinstatement of the addictive behavior (relapse), even after prolonged periods of abstinence.

Here we consider computational models of addiction based on reinforcement learning algorithms, Bayesian inference and biophysical neural simulations, with a focus on ‘model-free’ and ‘model-based’ aberrant control. We discuss whether computational models originally conceived to describe substance use disorders could be validly extended to behavioral addictions and we present computational models that have been specifically developed to describe maladaptive behaviors in behavioral addictions.