Disorders of compulsivity: a common bias towards learning habits

Voon, Valerie; Derbyshire, Katherine; Rück, Christian; Irvine, MA; Worbe, Yulia; Enander, Jesper; Schreiber, Lrn; Gillan, Claire M.; Fineberg, NA; Sahakian, Barbara J.; Robbins, Trevor W.; Harrison, Neil A.; Wood, Jonathan; Daw, Nathaniel D.; Dayan, Peter; Grant, JE; Bullmore, Edward T.

doi:10.1038/mp.2014.44

Cited by 545 publications

(629 citation statements)

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“…There is accumulating evidence that in substance dependence and disorders of compulsivity PIT effects are increased (Garbusow et al, , 2015Hogarth, Field, & Rose, 2013;Glasner, Overmier, & Balleine, 2005) whereas MB control appears to be disrupted Voon et al, 2014). Moreover, MB neural signatures are reduced in high-impulsive individuals (Deserno, Wilbertz, et al, 2015), and impulsivity further seems to be associated with PIT effects (Garofalo & di Pellegrino, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Doll et al, 2016). Most studies have found correlations with the MB but not with the MF component, including cognitive Otto et al, 2013) and emotional (Otto et al, 2013) variables as well as pharmacological challenges Wunderlich, Smittenaar, & Dolan, 2012), brain stimulation (Smittenaar, FitzGerald, Romei, Wright, & Dolan, 2013; but see Smittenaar, Prichard, FitzGerald, Diedrichsen, & Dolan, 2014), and interindividual differences such as age (Eppinger, Walter, Heekeren, & Li, 2013) or psychiatric disorders Voon et al, 2014). Other tasks such as the probabilistic selection task may be more appropriate to specifically assess the MF system (Doll et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

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Don't Think, Just Feel the Music: Individuals with Strong Pavlovian-to-Instrumental Transfer Effects Rely Less on Model-based Reinforcement Learning

Sebold

Schad

Nebe

et al. 2016

Journal of Cognitive Neuroscience

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Behavioral choice can be characterized along two axes. One axis distinguishes reflexive, modelfree systems that slowly accumulate values through experience and a model-based system that uses knowledge to reason prospectively. The second axis distinguishes Pavlovian valuation of stimuli from instrumental valuation of actions or stimulus-action pairs. This results in four values and many possible interactions between them, with important consequences for accounts of individual variation. We here explored whether individual variation along one axis was related to individual variation along the other. Specifically, we asked whether individuals' balance between model-based and model-free learning was related to their tendency to show Pavlovian interferences with instrumental decisions. In two independent samples with a total of 243 participants, Pavlovian-instrumental transfer effects were negatively correlated with the strength of model-based reasoning in a two-step task. This suggests a potential common underlying substrate predisposing individuals to both have strong Pavlovian interference and be less model-based and provides a framework within which to interpret the observation of both effects in addiction. Abstract ■ Behavioral choice can be characterized along two axes. One axis distinguishes reflexive, model-free systems that slowly accumulate values through experience and a model-based system that uses knowledge to reason prospectively. The second axis distinguishes Pavlovian valuation of stimuli from instrumental valuation of actions or stimulus-action pairs. This results in four values and many possible interactions between them, with important consequences for accounts of individual variation. We here explored whether individual variation along one axis was related to individual variation along the other. Specifically, we asked whether individuals' balance between model-based and model-free learning was related to their tendency to show Pavlovian interferences with instrumental decisions. In two independent samples with a total of 243 participants, Pavlovianinstrumental transfer effects were negatively correlated with the strength of model-based reasoning in a two-step task. This suggests a potential common underlying substrate predisposing individuals to both have strong Pavlovian interference and be less model-based and provides a framework within which to interpret the observation of both effects in addiction. ■

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Don't Think, Just Feel the Music: Individuals with Strong Pavlovian-to-Instrumental Transfer Effects Rely Less on Model-based Reinforcement Learning

Sebold

Schad

Nebe

et al. 2016

Journal of Cognitive Neuroscience

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“…Using this framework, it was found that repeated exposure to addictive drugs shifts behavior from model-based to model-free emphasis (Kurth-Nelson and Redish 2011, Lucantonio et al 2012. Likewise, data fitting by the dual-system model revealed that subjects diagnosed with obsessivecompulsive disorder display a bias towards model-free habit acquisition (Voon et al 2014). The central control mechanisms governing the balance maintenance and shifts between model-based and model-free systems represent an area of intense ongoing research (Simon and Daw 2011).…”

Section: Endophenotypes Across Brain Disorder Categoriesmentioning

confidence: 99%

Computational Psychiatry

2016

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Psychiatric disorders such as autism and schizophrenia arise from abnormalities in brain systems that underlie cognitive, emotional and social functions. The brain is enormously complex and its abundant feedback loops on multiple scales preclude intuitive explication of circuit functions. In close interplay with experiments, theory and computational modeling are essential for understanding how, precisely, neural circuits generate flexible behaviors and their impairments give rise to psychiatric symptoms. This Perspective highlights recent progress in applying computational neuroscience to the study of mental disorders. We outline basic approaches, including identification of core deficits that cut across disease categories, biologically-realistic modeling bridging cellular and synaptic mechanisms with behavior, model-aided diagnosis. The need for new research strategies in psychiatry is urgent. Computational psychiatry potentially provides powerful tools for elucidating pathophysiology that may inform both diagnosis and treatment. To achieve this promise will require investment in cross-disciplinary training and research in this nascent field. © 2014 Elsevier Inc. All rights reserved. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Neither Dr. Wang nor Dr. Krystal has any financial interests relevant to the advancement of computational psychiatry. to and engage with psychiatric research. Second, it will be important to develop a cadre of young psychiatrists who learn computational modeling, lest computational psychiatry.

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“…Indeed, addictive behaviours and social cognition can be tied together through existing theoretical accounts of addictive behaviours -for example, those based on reinforcement learning 6 and the 'Bayesian brain' hypothesis 7 . Computational modelling has begun to inform empirical studies on decision making in addiction 8,9 , putatively addiction-like disorders 9,10 and risk factors for addiction [11][12][13] . Likewise, computational accounts of social cognition have been tested in neuroscience.…”

Section: I N K To O R I G I N a L A Rt I C L Ementioning

confidence: 99%

Linking social context and addiction neuroscience: a computational psychiatry approach

2017

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In their recent article (Time to connect: bringing social context into addiction neuroscience (Nat. Rev. Neurosci. 17, 592-599 (2016)) 1 , Heilig et al. suggest that the lack of progress towards treatment and prevention of addiction is partly due to a neglect of social factors in neuroscientific research of addiction. We share the authors' disappointment but argue that merely broadening the focus towards social context is not sufficient to close the "large gap [that] exists between the promise of neuroscientific approaches to addiction and what they have delivered". To date, we lack sufficient transfer of powerful theoretical accounts and methodological resources to understand the multiple facets of addiction. Thus, adding yet another obviously important component to the heterogeneity that characterizes addiction might be insufficient to make addiction neuroscience more clinically relevant. Rather, a conceptual shift in 'addiction neuroscience' may be warranted.Seminal epidemiological and neuroimaging studies have established associations between neurobiological measures, addiction and social status 2-4 , thereby generating hypotheses on the physiological, psychological and social components of addiction. In our view, the next step should be to determine how different neural computations in modulatory circuits give rise to certain psychological phenomena of learning and decision making that are associated with addictive behaviours, such as cue-induced drug craving and habitual drug intake despite negative consequences. To integrate these interacting physiological, behavioural and social levels of description 5 , a generative model of addictive behaviours is needed. To achieve this goal, we propose that coherent theoretical accounts be formalized and applied in stringent and translational experimental design. A developmental perspective is necessary to track the onset, maintenance and relapse of addiction in longitudinal studies. Computational models might prove valuable for any of these processes.Computational models aid theory building by formalizing hypotheses and rigorously capture relations between latent factors and observations. They can inform on particularly meaningful manipulations and enable mechanistic interpretation of experimental results. Indeed, addictive behaviours and social cognition can be tied together through existing theoretical accounts of addictive behaviours -for example, those based on reinforcement learning 6 and the 'Bayesian brain' hypothesis 7 . Computational modelling has begun to inform empirical studies on decision making in addiction 8,9 , putatively addiction-like disorders 9,10 and risk factors for addiction [11][12][13] . Likewise, computational accounts of social cognition have been tested in neuroscience. Research combining computational modelling of social behaviours with neuroimaging suggests that social information may be processed by mechanisms similar to those involved in (non-social) reward-based learning and decision making 14 . Thus, establishing a mechanistic theo...

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Disorders of compulsivity: a common bias towards learning habits

Cited by 545 publications

References 47 publications

Don't Think, Just Feel the Music: Individuals with Strong Pavlovian-to-Instrumental Transfer Effects Rely Less on Model-based Reinforcement Learning

Don't Think, Just Feel the Music: Individuals with Strong Pavlovian-to-Instrumental Transfer Effects Rely Less on Model-based Reinforcement Learning

Computational Psychiatry

Linking social context and addiction neuroscience: a computational psychiatry approach

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