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July 1, 2020

Translation of Computational Psychiatry in the Context of Addiction

Author Affiliations
  • 1Department of Psychiatry and Psychotherapy, Charité—Universitätsmedizin Berlin (Campus Charité Mitte), Berlin, Germany
  • 2Max Planck Centre for Computational Psychiatry and Ageing Research & Wellcome Centre for Human Neuroimaging, University College London, London, England
JAMA Psychiatry. 2020;77(11):1099-1100. doi:10.1001/jamapsychiatry.2020.1637
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    1 Comment for this article
    Modelling the Future of Treatment in Addiction
    Alex Robinson, PhD Candidate (Clin Psyc) | Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
    In their Viewpoint, Liu et al [1] illustrate the promise of computational psychiatry in advancing addiction research. We agree that the model-based/model-free framework has great utility in bridging preclinical and clinical research. However, a priority will be to delineate the impact of each process on the vulnerability to, and recovery from, substance use disorders.

    Model-based and model-free decision-making are often used as proxies for habitual and goal-directed behaviour, respectively. This is because the model-free system solely learns from prior outcomes (through reward prediction errors), while the model-based system prospectively forecasts outcomes using state-transition probabilities [2]. Addiction researchers often
    pit model-based and model-free processes as diametrically opposed systems [3]. However, model-based algorithms also often incorporate elements of previous reward value [2].

    This overlap has important implications for disorders of addiction, in which reward learning is often dysfunctional. For example, studies in methamphetamine use disorder that assumed that model-based and model-free decision-making were orthogonal found a bias towards model-free learning. This result is often interpreted as evidence of “habitual” or “compulsive” behaviour amongst this population [3]. However, because these findings only described the relative weighting of model-based relative to model-free learning, it is difficult to establish whether the individual processes are themselves preserved or dysfunctional.

    In contrast, preclinical research that have not assumed an independence of model-based and model-free learning have shown deficits in both processes in models of methamphetamine use disorder [4]. Furthermore, such approaches have identified that lower model-free functioning (prior to drug administration) predicted subsequent methamphetamine seeking [4]. Such findings lead to a different interpretation to that above – that disruption to reward learning is at least as relevant as the propensity towards habit formation in the development of substance use disorders.

    The independence, or otherwise, of model-based and model-free learning particularly impacts how we translate this framework into novel treatment approaches. For example, clients with model-based deficits may benefit from strategies that strengthen state-related predictions and goal-orientated decisions [5]. In contrast, clients who have both model-based and model-free deficits may benefit from strategies that specifically account for their reduced reward learning to scaffold subsequent goal-orientated learning (e.g., Contingency Management) [5].

    Therefore, while we agree with Liu et al [1] on the potential utility of the model-based/model-free framework, it remains for addiction researchers to characterise the functional consequences of these systems. Specifically, we argue that assessing both model-based and model-free decision-making across the lifespan of substance use disorders will aid the development of more effective interventions.

    Authors: Alex H. Robinson, Trevor T.-J. Chong, Antonio Verdejo-Garcia


    1. Liu S, Dolan RJ, Heinz A. 2020. doi:10.1001/jamapsychiatry.2020.1637

    2. Daw ND, Gershman SJ, Seymour B, Dayan P, Dolan RJ. 2011. doi:10.1016/j.neuron.2011.02.027

    3. Voon V, Derbyshire K, Rück C, et al. 2015. doi:10.1038/mp.2014.44

    4. Groman SM, Massi B, Mathias SR, Lee D, Taylor JR. 2019. doi:10.1016/j.biopsych.2018.12.017

    5. Verdejo-García A, Alcázar-Córcoles MA, Albein-Urios N. 2019. doi:10.1007/s11065-018-9384-6
    CONFLICT OF INTEREST: AHR is supported by an Australian Government Research Training Program Scholarship; TC is supported by the Australian Research Council (DP 180102383, DE 180100389), the Judith Jane Mason and Harold Stannett Williams Memorial Foundation, the Brain Foundation, the Rebecca L Cooper Medical Research Foundation, and the Society for Mental Health Research; AVG is is funded by an Australian Medical Research Future Fund fellowship (MRF1141214). He has received honorarium from Servier for the preparation of a literature review, and from Elsevier for editing work. He is part of the scientific advisory board of Monclarity (Brainwell), but does not receive honorarium.