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Publications

Computational Psychiatry & Decision-making

Papers, reviews & chapters

    2017

  • The neural basis of aversive Pavlovian guidance during planning
  • Lally* N, Huys* QJM, Eshel N, Faulkner P, Dayan P and Roiser JP
  • J. Neurosci. (2017) In Press
  • Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multi-step decisions. Therefore, humans frequently employ heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes. However, how the brain implements this important decision heuristic, and what underlies individual differences in its strength have hitherto remained unanswered. Therefore, we administered an adapted version of the same planning task to healthy volunteers undergoing functional magnetic resonance imaging (fMRI) to determine the neural basis of aversive pruning. Through both computational and standard categorical fMRI analyses, we show that when planning was influenced by aversive pruning, the subgenual cingulate cortex was robustly recruited. This neural signature was distinct from those associated with general planning and valuation, two fundamental cognitive components elicited by our task but which are complementary to aversive pruning. Furthermore, we found that individual variation in levels of aversive pruning were associated with the responses of insula and dorsolateral prefrontal cortex to the receipt of large monetary losses, and also with sub-clinical levels of anxiety. In summary, our data reveal the neural signatures of an important reflexive Pavlovian processes that shapes goal-directed evaluations, and thereby determine the outcome of high-level sequential cognitive processes.
  • A formal valuation framework for emotions and their control
  • Huys QJM and Renz D
  • Biological Psychiatry (2017) In Press
  • Computational psychiatry attempts to apply mathematical and computational techniques to help improve psychiatric care. Here, we consider formal valuation accounts of emotion. The flexibility of emotional responses and the nature of appraisals suggest the need for a model-based framework for emotions. Resource limitations make plain model-based valuation impossible, and require strategies to apportion cognitive resources adaptively. We argue that emotions can implement such approximations by restricting the range of behaviours and states considered. We consider the processes that guide the deployment of the emotional approximations, discerning between innate, model-free, heuristic and model-based controllers. A focus on complex model-based decisions reveals the necessity for strategies to deal with the complexity of the problems. Emotions may provide such approximations, and this framework may provide a principled approach to examining them.
  • Strong seduction - Impulsivity and the impact of contextual cues on instrumental behavior in alcohol dependence
  • Sommer C, Garbusow M, Jünger E, Pooseh S, Bernhardt N, Birkenstock J, Schad D, Jabs B, Glöckler T, Huys QJM, Heinz A, and Smolka M and Zimmermann US
  • Translational Psychiatry (2017) In Press
  • Alcohol-related cues acquire incentive salience through Pavlovian conditioning and then can markedly affect instrumental behavior of alcohol-dependent patients to promote relapse. However, it is unclear whether similar effects occur with alcohol-unrelated cues. We tested 116 early abstinent alcohol-dependent patients and 91 healthy controls who completed a delay discounting task to assess choice impulsivity, and a Pavlovian-to-instrumental transfer (PIT) paradigm employing both alcohol-unrelated and alcohol-related stimuli. To modify instrumental choice behavior, we tiled the background of the computer screen either with conditioned stimuli (CS) previously generated by pairing abstract pictures with pictures indicating monetary gains or losses, or with pictures displaying alcohol or water beverages. CS paired to money gains and losses affected instrumental choices differently. This PIT effect was significantly more pronounced in patients compared to controls, and the group difference was mainly driven by highly impulsive patients. The PIT effect was particularly strong in trials in which the instrumental stimulus required inhibition of instrumental response behavior and the background CS was associated to monetary gains. Under that condition, patients performed inappropriate approach behavior, contrary to their previously formed behavioral intention. Surprisingly, the effect of alcohol and water pictures as background stimuli resembled that of aversive and appetitive CS, respectively. These findings suggest that positively valenced background CS can provoke dysfunctional instrumental approach behavior in impulsive alcohol-dependent patients. Consequently, in real life they might be easily seduced by environmental cues to engage in actions thwarting their long-term goals. Such behaviors may include, but are not limited to, approaching alcohol.
  • The impact of traumatic stress on Pavlovian biases
  • Ousdal OT*, Huys QJM*, Milde AM, Craven AR, Ersland L, Endestad T, Melinder A, Hugdahl K, Dolan RJ
  • Psychol Med (2017)
  • When habits are dangerous: alcohol expectancies and habitual decision-making in alcohol dependence
  • Sebold M, Nebe S, Garbusow M, Schad DJ, Beck A, Kuitunen-Paul S, Sommer C, Neu P, Zimmermann US, Rapp MA, Smolka MN, Huys QJM, Schlagenhauf F, Heinz A
  • Biological Psychiatry (2017) In Press
  • Addiction is supposed to be characterized by a shift from goal-directed to habitual decision-making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modelling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision-making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based vs. model-free decision-making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients. Ninety detoxified, medication-free alcohol-dependent patients and 96 age- and gender-matched control underwent functional magnetic resonance imaging during the two-step task. Alcohol expectancies were measured with the Alcohol Expectancy Questionnaire.. Over a follow-up period of 48 weeks, 37 patients remained abstinent whereas 53 patients. Patients who relapsed displayed reduced medial prefrontal cortex (mPFC) activation during model-based decision-making. Furthermore high alcohol expectancies were associated with low model-based control in relapsers, while the opposite was observed in abstainers and healthy controls. However, reduced model-based control per se was not associated with subsequent relapse. These findings suggest that poor treatment outcome in addicted patients does not simply result from reduced model-based control but is rather dependent on the interaction between high drug expectancies and low model-based decision-making. Reduced model-based mPFC signatures in prospective relapsers point to a neural correlate of relapse risk. These observations suggest that therapeutic interventions should target subjective alcohol expectancies.
  • doi pdf No association of goal-directed and habitual control with alcohol consumption in young adults
  • Nebe S, Kroemer NB, Schad DJ, Bernhardt N, Sebold M, Müller DK, Scholl L, Kuitunen-Paul S, Heinz A, Rapp M, Huys QJ, Smolka MN
  • Addiction Biology (2017)
  • Alcohol dependence is a mental disorder which has been associated with an imbalance in behavioral control favoring model-free habitual over model-based goal-directed strategies. It is as yet unknown, however, whether such an imbalance reflects a predisposing vulnerability or results as a consequence of repeated and/or excessive alcohol exposure. We, therefore, examined the association of alcohol consumption with model-based goal-directed and model-free habitual control in 188 eighteen-year-old social drinkers in a two-step sequential decision-making task while undergoing fMRI before prolonged alcohol misuse could have led to severe neurobiological adaptations. Behaviorally, participants showed a mixture of model-free and model-based decision-making as observed previously. Measures of impulsivity were positively related to alcohol consumption. In contrast, neither model-free nor model-based decision weights nor the tradeoff between them were associated with alcohol consumption. There were also no significant associations between alcohol consumption and neural correlates of model-free or model-based decision quantities in either ventral striatum or ventromedial prefrontal cortex. Exploratory whole-brain fMRI analyses with a lenient threshold revealed early onset of drinking to be associated with an enhanced representation of model-free reward prediction errors in the posterior putamen. These results suggest that an imbalance between model-based goal-directed and model-free habitual control might rather not be a trait marker of alcohol intake per se.
  • doi pdf The anchoring bias reflects rational use of cognitive resources.
  • Lieder F, Griffiths TL, Huys QJM and Goodman ND
  • Psychonomic Bulletin & Review (2017)
  • Cognitive biases, such as the anchoring bias, pose a serious challenge to rational accounts of human cognition. We investigate whether rational theories can meet this challenge by taking into account the mind's bounded cognitive resources. We asked what reasoning under uncertainty would look like if people made rational use of their finite time and limited cognitive resources. To answer this question, we applied a mathematical theory of bounded rationality to the problem of numerical estimation. Our analysis led to a rational process model that can be interpreted in terms of anchoring-and-adjustment. This model provided a unifying explanation for ten anchoring phenomena including the differential effect of accuracy motivation on the bias towards provided versus self-generated anchors. Our results illustrate the potential of resource-rational analysis to provide formal theories that can unify a wide range of empirical results and reconcile the impressive capacities of the human mind with its apparently irrational cognitive biases.
  • pdf Empirical evidence for resource-rational anchoring and adjustment
  • Lieder F, Griffiths TL, Huys QJM and Goodman ND
  • Psychonomic Bulletin & Review (2017)
  • People's estimates of numerical quantities are systematically biased towards their initial guess. This anchoring bias is usually interpreted as sign of human irrationality, but it has recently been suggested that the anchoring bias instead results from people's rational use of their finite time and limited cognitive resources. If this were true, then adjustment should decrease with the relative cost of time. To test this hypothesis, we designed a new numerical estimation paradigm that controls people's knowledge and varies the cost of time and error independently while allowing people to invest as much or as little time and effort into refining their estimate as they wish. Two experiments confirmed the prediction that adjustment decreases with time cost but increases with error cost regardless of whether the anchor was self-generated or provided. These results support the hypothesis that people rationally adapt their number of adjustments to achieve a near-optimal speed-accuracy tradeoff. This suggests that the anchoring bias might be a signature of the rational use of finite time and limited cognitive resources rather than a sign of human irrationality.
  • doi pdf Value-based decision-making battery: A Bayesian adaptive approach to assess impulsive and risky behavior
  • Pooseh S, Bernhardt N, Guevara A, Huys QJM and Smolka MN
  • Behav Res Methods (2017)
  • Using simple mathematical models of choice behavior, we present a Bayesian adaptive algorithm to assess measures of impulsive and risky decision making. Practically, these measures are characterized by discounting rates and are used to classify individuals or population groups, to distinguish unhealthy behavior, and to predict developmental courses. However, a constant demand for improved tools to assess these constructs remains unanswered. The algorithm is based on trial-by-trial observations. At each step, a choice is made between immediate (certain) and delayed (risky) options. Then the current parameter estimates are updated by the likelihood of observing the choice, and the next offers are provided from the indifference point, so that they will acquire the most informative data based on the current parameter estimates. The procedure continues for a certain number of trials in order to reach a stable estimation. The algorithm is discussed in detail for the delay discounting case, and results from decision making under risk for gains, losses, and mixed prospects are also provided. Simulated experiments using prescribed parameter values were performed to justify the algorithm in terms of the reproducibility of its parameters for individual assessments, and to test the reliability of the estimation procedure in a group-level analysis. The algorithm was implemented as an experimental battery to measure temporal and probability discounting rates together with loss aversion, and was tested on a healthy participant sample.
  • doi pdf Predicting relapse after antidepressant withdrawal - a systematic review
  • Berwian IM, Walter H, Seifritz E, Huys QJM
  • Psychol. Med. (2017) 47(3):426-437
  • Background: A substantial proportion of the burden of depression arises from its recurrent nature. The risk of relapse after antidepressant (ADM) discontinuation is high but not uniform. Predictors of individual relapse risk after antidepressant discontinuation could help to guide treatment and mitigate the long-term course of depression.

    Methods: We conducted a systematic literature search in Pubmed to identify relapse predictors using the search terms "(depress* OR MDD*) AND (relapse* OR recurren*) AND (predict* OR risk) AND (discontinu* OR withdraw* OR maintenance OR maintain or continu*) AND (antidepress* OR medication OR drug)" for published studies until November 2014. Studies investigating predictors of relapse in patients aged between 18 and 65 with a main diagnosis of Major Depressive Disorder (MDD) who remitted from a depressive episode while treated with antidepressant medication and were followed up for at least 6 months to assess relapse after part of the sample discontinued their ADM, were included in the review.

    Results: Although relevant information is present in many studies, only thirteen studies based on nine separate samples investigated predictors for relapse after ADM discontinuation. There are multiple promising predictors, including markers of true treatment response and the number of prior episodes. However, the existing evidence is weak and there are no established, validated markers of individual relapse risk after antidepressant cessation.

    Conclusion: There is little evidence to guide discontinuation decisions in an individualized manner beyond overall recurrence risk. Thus, there is a pressing need to investigate neurobiological markers of individual relapse risk, focusing on treatment discontinuation.

  • 2016

  • pdf A valuation framework for emotions applied to depression and recurrence
  • Huys QJM
  • In: Computational Psychiatry: New Perspectives on Mental Illness, edited by A. D. Redish and J. A. Gordon. Strüngmann Forum Reports, vol. 20, J. Lupp, series editor. Cambridge, MA: MIT Press
  • The burden of depression is substantially aggravated by relapses and recurrences, and these become more inevitable with every episode of depression. This chapter first describes how computational psychiatry can provide a normative framework for emotions that might provide an integrative approach to core cognitive components of depression and relapse. At the heart of this account is the notion that emotions effectively imply a valuation, and that they are therefore amenable to description and dissection by reinforcement-learning methods. It is argued that cognitive accounts of emotion can be viewed in terms of model-based valuation, and that automatic emotional responses relate to model-free valuation and the innate recruitment of fixed behavioural patterns. The model-based view captures phenomena such as helplessness, hopelessness, attributions and stress sensitization. Considering it in more atomic algorithmic detail opens up the possibility of viewing rumination and emotion regulation in this same normative framework, too. The chapter then briefly outlines the problem of treatment selection for relapse and recurrence prevention, and then suggests ways in which the computational framework of emotions might help in improving this. The discussion closes with a very brief general overview over what we can hope to gain from computational psychiatry.

     
     

  • pdf Complexity and Heterogeneity in psychiatric disorders.
  • Totah N, Akil H, Huys QJM, Krystal JH, MacDonald III AW, Maia TV, Malenka RC and Pauli WM
  • In: Computational Psychiatry: New Perspectives on Mental Illness, edited by A. D. Redish and J. A. Gordon. Strüngmann Forum Reports, vol. 20, J. Lupp, series editor. Cambridge, MA: MIT Press
  • Psychiatry faces a number of challenges, among them are the reconceptualization of symptoms and diagnoses, disease prevention, treatment development and monitoring of its effects, and the provision of individualized, precision medicine. Achieving these goals will require an increase in the biological, quantitative, and theoretical grounding of psychiatry. To address these challenges, psychiatry must confront the complexity and heterogeneity intrinsic to the nature of brain disorders. This chapter seeks to identify the sources of complexity and heterogeneity as a means of confronting the challenges facing the field. These sources include the interplay between genetic and epigenetic factors with the environment and their impact on neural circuits. Moreover, these interactions are expressed dynamically over the course of development and continue to play out during the disease process and treatment.

    We propose that computational approaches provide a framework for addressing the complexity and heterogeneity that underlie the challenges facing psychiatry. Central to our argument is the idea that these characteristics are not noise to be eliminated from diagnosis and treatment of disorders. Instead, such complexity and heterogeneity arises from intrinsic features of brain function and, therefore, represent opportunities for computational models to provide a more accurate biological foundation for diagnosis and treatment of psychiatric disorders. The challenges to be addressed by a computational framework include the following. First, it must improve the search for risk factors and biomarkers, which can be used toward primary prevention of disease. Second, it must help to represent the biological ground truth of psychiatric disorders, which will improve the accuracy of diagnostic categories, assist in discovering new treatments, and aid in precision medicine. Third, to be useful for secondary prevention, it must represent how risk factors, biomarkers, and the underlying biology change through the course of development, disease progression, and treatment process.

     
     

  • preprint How do emotion and cognition interact?
  • Cools R, den Ouden HE and Huys QJM
  • In Fox AS et al., (eds), The nature of emotion, 2nd edition (2016) In Press
  • pdf Computational Psychiatry
  • Huys QJM
  • Zeitschrift für Psychiatrie, Psychologie und Psychotherapie. 65(1):21--26.
  • "Computational Psychiatry" ist eine neue Forschungsrichtung, die Fortschritte aus den theoretischen und experimentellen Neurowissenschaften in klinische Anwendungen für die Psychiatrie umzusetzen will. Der mögliche Nutzen mathematischer Modelle für psychiatrische Anwendungen ergibt sich vor allem aus der Komplexität psychiatrischer Phänomene, deren Beherrschung neue analytische Herangehensweisen erfordert. Konkret können mithilfe solcher Modelle erstens innerpsychische und ansonsten nicht direkt messbare Prozesse erfasst werden. Ein Beispiel hierfür sind Lernprozesse. Zweitens können Phänomene auf verschiedenen Ebenen quantitativ miteinander in Verbindung gebracht werden, z.B. der Effekt von Ionenkanalstörungen auf das Kurzzeitgedächtnis. Drittens können Methoden aus dem maschinellen Lernen mit diesen Modellen verbunden werden, um grosse Datensätze zu analysieren. Obwohl erste Ansätze aus dieser Forschung schon möglichen klinischen Nutzen erwiesen haben, ist das Feld noch jung. Der Artikel schliesst mit dem Vorschlag, Prozeduren aus der Entwicklung pharmazeutischer Produkte für die Validierung theoretischer Anwendungen herbeizuziehen.

  • doi pdf Computational psychiatry: from mechanistic insights to the development of new treatments
  • Huys QJM, Maia T and Paulus MP
  • Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (2016) 1(5):382-385
  • Computational psychiatry is a young field that aims to further our understanding of mental illness and its treatment with the use of novel computational techniques. The present issue provides an overview over the breadth of the field. On the one hand, computational techniques can be used to provide mechanistic insight into illnesses. This is exemplified with contributions using Bayesian and reinforcement-learning techniques into schizophrenia, methamphetamine and alcohol use disorders. On the other hand, mechanistically agnostic techniques can directly infer information relevant to treatment. Examples in the issue include prediction of depression treatment responses with EEG and response prediction with fMRI. The issue concludes with a novel way to address heterogeneity, and finally with a proposal to adopt a developmental pathway akin to that in drug development to ensure computational psychiatry fulfills its promise to improve patient outcomes.

  • doi pdf Model-free temporal-difference learning and dopamine in alcohol dependence: examining concepts from theory and animals in human imaging
  • Huys QJM, Deserno L, Obermeyer K, Schlagenhauf F, Heinz A
  • Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (2016) 1(5):401-410
  • Dopamine potentially unites two important roles: one in addiction, being involved in most substances of abuse including alcohol, and a second one in a specific type of learning, namely model-free temporal-difference reinforce- ment learning. Theories of addiction have long suggested that drugs of abuse may usurp dopamine's role in learning. We here briefly review the preclinical literature to motivate specific hypotheses about model-free temporal-difference learning, and then review the imaging evidence in the drug of abuse with the most substantial societal consequences: alcohol. Despite the breadth of the literature, only very few studies have examined the predictions directly, and these provide at best inconclusive evidence for the involvement of temporal-difference learning alterations in alcohol de- pendence. We discuss the difficulties of testing the theory, make specific suggestions and close with a focus on the interaction with other learning mechanisms.

  • doi pdf A Roadmap for the Development of Applied Computational Psychiatry
  • Paulus MP, Huys QJM and Maia T
  • Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (2016) 1(5):386-392
  • Background: Computational psychiatry is a burgeoning field that utilizes mathematical approaches to investigate psychiatric disorders, derive quantitative predictions, and integrate data across multiple levels of description. Computational psychiatry has already led to many new insights into the neurobehavioral mechanisms that underlie several psychiatric disorders, but its usefulness from a clinical standpoint is only now starting to be considered. Methods: Examples of computational psychiatry are highlighted, and a phase-based pipeline for the development of clinical computational- psychiatry applications is proposed, similar to the phase-based pipeline used in drug development. It is proposed that each phase has unique endpoints and deliverables, which will be important milestones to move tasks, procedures, computational models, and algorithms from the laboratory to clinical practice. Results: Application of computational approaches should be tested on healthy volunteers in Phase I, transitioned to target populations in Phase IB and Phase IIA, and thoroughly evaluated using randomized clinical trials in Phase IIB and Phase III. Successful completion of these phases should be the basis of determining whether computational models are useful tools for prognosis, diagnosis, or treatment of psychiatric patients. Conclusions: A new type of infrastructure will be necessary to implement the proposed pipeline. This infrastructure should consist of groups of investigators with diverse backgrounds collaborating to make computational psychiatry relevant for the clinic.

  • doi pdf Computational neuroimaging strategies for single patient predictions
  • Stephan KE, Schlagenhauf F, Huys QJ, Raman S, Aponte EA, Brodersen KH, Rigoux L, Moran RJ, Daunizeau J, Dolan RJ, Friston KJ and Heinz A
  • Neuroimage (2017) 145(Pt B):180-199
  • Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.
  • doi pdf Don't think, just feel the music: Individuals With Strong Pavlovian-to-instrumental Transfer Effects Rely Less on Model-based Reinforcement Learning
  • Sebold M, Schad DJ, Nebe S, Garbusow M, Jünger E, Krömer N, Kathmann N, Zimmermann U, Smolka M, Rapp MA, Heinz A, Huys QJM
  • J Cogn Neurosci (2016) 28(7):985--995
  • 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 a quartet of 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 individual's 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 subjects, 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.
  • arxiv pdf Better safe than sorry: Risky function exploitation through safe optimization
  • Schulz E, Huys QJM, Bach DR, Speekenbrink M, Krause A
  • Cog Sci 2016
  • Exploration-exploitation of functions, that is learning and optimizing a mapping between inputs and expected outputs, is ubiquitous to many real world situations. These situations sometimes require us to avoid certain outcomes at all cost, for example because they are poisonous, harmful, or otherwise dangerous. We test participants' behavior in scenarios in which they have to find the optimum of a function while at the same time avoid outputs below a certain threshold. In two experiments, we find that Safe-Optimization, a Gaussian Process-based exploration-exploitation algorithm, describes participants' behavior well and that participants seem to care firstly whether a point is safe and then try to pick the optimal point from all such safe points. This means that their trade-off between exploration and exploitation can be seen as an intelligent, approximate, and homeostasis-driven strategy.

  • data / code doi pdf German translation and validation of the Cognitive Style Questionnaire short form (CSQ-SF-D)
  • Huys QJM, Renz D, Petzschner R, Berwian I and Haker H
  • PLoS One (2016) 11(3):e0149530
  • Background: The Cognitive Style Questionnaire is a valuable tool for the assessment of hopeless cognitive styles in depression research, with predictive power in longitudinal studies. Even the short form is still long, and neither this nor the original version exist in validated German translations.

    Methods: The questionnaire was translated from English to German, back-translated and commented on by clinicians. The reliability, factor structure and external validity of an online form of the questionnaire were examined on 214 participants. External validity was measured on a subset of 90 subjects.

    Results: The resulting CSQ-SF-D had good to excellent reliability, both across items and subscales, and similar external validity to the original English version. The internality subscale appeared less robust than other subscales. A detailed analysis of individual item performance suggests that stable results could be achieved with a very short form (CSQ-VSF-D) including only 27 items.

    Conclusions: The CSQ-SF-D is a validated and freely distributed translation of the CSQ-SF into German. This should make efficient assessment of cognitive style in German samples more accessible to researchers.

  • Rückfallvorhersage bei Depressionen: welche Rolle spielen Schuldgefühle?
  • Huys QJM
  • Info Neurologie und Psychiatrie (2016) In Press
  • pdf Rückfallvorhersage nach Absetzen von Antidepressiva
  • Berwian IM, Seifritz E, Walter H, Huys QJM
  • Info Neurologie und Psychiatrie (2016) 14(2):16-19
    • Nicht alle depressiven Patienten in Remission profitieren von Antidepressiva in Bezug auf R¨ckfallprophylaxe.
    • Viele Patienten wollen ihr Antidepressivum absetzen, unter anderem wegen unerwünschten Nebenwirkungen.
    • Zurzeit gibt es keine Prädiktoren, die vorhersagen, welcher Patient einen Rückfall bekommt und welcher nicht.
    • Möglicherweise können neurobiologische Biomarker als Prädiktoren dienen.
    • Die Antidepressiva-Absetzstudie (AIDA) untersucht Prädiktoren, die ein sicheres Absetzen vorhersagen; dazu werden weiterhin Patienten gesucht, die in Remission sind und ihr Antidepressivum absetzen wollen.
  • doi pdf Computational psychiatry as a bridge from neuroscience to clinical applications
  • Huys QJM*, Maia T* and Frank MJ
  • Nature Neuroscience (2016) 19(3):404--413
    Commentary in Scientific American
  • Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ----the brain---and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction, and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data-driven and theory-driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes, or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.

  • 2015

  • doi pdf The specificity of Pavlovian regulation is associated with recovery from depression
  • Huys QJM, Gölzer M, Friedel E, Heinz A, Cools R, Dayan P and Dolan RJ
  • Psychological Medicine (2016) 46(5):1027-1035
  • Background: Changes in reflexive emotional responses are hallmarks of depression, but how emotional reflexes impact on adaptive decision-making in depression has not been examined formally. Using a Pavlovian-Instrumental Transfer (PIT) task, we compared the influence of affectively valenced stimuli on decision-making in depression and generalized anxiety disorder compared with healthy controls; and related this to the longitudinal course of the illness.

    Methods: Fourty subjects with a current DSM-IV-TR diagnosis of Major Depressive Disorder, Dysthymia, Generalised Anxiety Disorder or a combination thereof, and 40 matched healthy controls performed a Pavlovian-instrumental transfer (PIT) task that assesses how instrumental approach and withdrawal behaviours are influenced by appetitive and aversive Pavlovian conditioned stimuli. Patients were followed up after 4-6 months. Analyses focussed on patients with depression alone (n=25).

    Results: In healthy controls, Pavlovian conditioned stimuli (CSs) exerted action-specific effects, with appetitive CSs boosting active approach and aversive CSs withdrawal. This action-specificity was absent in currently depressed subjects. Greater action-specificity in patients was associated with better recovery over the follow-up period.

    Conclusions: Depression is associated with abnormal influence of emotional reactions on decision-making in a way that is predictive of recovery.

  • doi pdf Charting the Landscape of Priority Problems in Psychiatry. Part 1: Nosology and Diagnosis
  • Stephan KE, Bach DR, Fletcher PC, Flint SJ, Frank MJ, Friston KJ, Heinz A, Huys QJM Owen MJ, Binder EB, Dayan P, Johnstone E, Meyer-Lindenberg A, Montague PR, Schnyder U, Wang XJ, Breakspear M
  • Lancet Psychiatry (2015)
  • Contemporary psychiatry faces major challenges. Its phenomenological nosology continues to lack mechanistic interpretability and predictive guidance; treatment largely depends on trial and error; drug development is impeded through ignorance of potential beneficiary subgroups; neuroscientific and genetics research has yet to impact disease definitions or to contribute to clinical decision-making. In this parlous setting, what should psychiatric research focus on?
    In two companion papers, we present a list of concrete problems nominated by clinicians and researchers from different disciplines as candidates for future scientific investigation of mental disorders. These problems are loosely grouped into challenges concerning nosology and diagnosis (this article) and those addressing pathogenesis and aetiology (in the companion article). Motivated by successful examples in other disciplines (particularly the famous list of "Hilbert's problems" in mathematics), this subjective and eclectic list of priority problems is intended to provide inspiration for the field, helping to refocus existing research and providing perspectives for future psychiatric science.
  • doi pdf Charting the Landscape of Priority Problems in Psychiatry. Part 2: Pathogenesis and Aetiology
  • Stephan KE, Binder EB, Breakspear M, Dayan P, Johnstone E, Meyer-Lindenberg A, Schnyder U, Wang XJ, Bach DR, Fletcher PC, Flint SJ, Frank MJ, Heinz A, Huys QJM, Montague PR, Owen MJ, Friston KJ
  • Lancet Psychiatry (2015)
  • This is the second of two companion papers proposing priority problems for research on mental disorders. Whereas the first article focuses on questions of nosology and diagnosis, the challenges articulated in this paper concern pathogenesis and aetiology of psychiatric diseases. We hope that this (non-exhaustive and subjective) list of problems, nominated by scientists and clinicians from different fields and institutions, provides guidance and perspectives for choosing future directions in psychiatric science.
  • doi pdf Computational Psychiatry: Towards a mathematically informed understanding of mental illness
  • Adams RA, Huys QJM and Roiser JP
  • J Neurol Neurosurg Psych (2016): 87(1):53-63
  • Computational Psychiatry aims to describe the relationship between the brain's neurobiology, its environment and mental symptoms in computational terms. In so doing, it may improve psychiatric classification and the diagnosis and treatment of mental illness. It can unite many levels of description in a mechanistic and rigorous fashion, whilst avoiding biological reductionism and artificial categorization. We describe how computational models of cognition can infer the current state of the environment and weigh up future actions, and how these models provide new perspectives on two example disorders, depression and schizophrenia. Reinforcement learning describes how the brain can choose and value courses of actions according to their long-term future value. Some depressive symptoms may result from aberrant valuations, which could arise from prior beliefs about the loss of agency ("helplessness"), or from an inability to inhibit the mental exploration of aversive events. Predictive coding explains how the brain might perform Bayesian inference about the state of its environment by combining sensory data with prior beliefs, each weighted according to their certainty (or precision). Several cortical abnormalities in schizophrenia might reduce precision at higher levels of the inferential hierarchy, biasing inference towards sensory data and away from prior beliefs. We discuss whether striatal hyperdopaminergia might have an adaptive function in this context, and also how reinforcement learning and incentive salience models may shed light on the disorder. Finally, we review some of Computational Psychiatry's applications to neurological disorders such as Parkinson's disease, and some pitfalls to avoid when applying its methods.
  • doi pdf Neural Correlates of Three Promising Endophenotypes of Depression: Evidence from the EMBARC Study
  • Webb CA, Gillon DG, Pechtel P, Goer F, Murray L, Huys QJM, Fava M, McGrath PJ , Weissman M, Parsey R, Kurian K, Adams P, Weyandt S, Trombello J, Grannemann B, Cooper C, Deldin P, Tenke C, Trivedi M, Bruder G and Pizzagalli DA
  • Neuropsychopharmacology (2016): 41(2):454-63
  • Major Depressive Disorder (MDD) is clinically, and likely pathophysiologically, heterogeneous. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes. Guided by the NIMH Research Domain Criteria (RDoC) initiative, we used source localization of scalp-recorded EEG resting data to examine the neural correlates of three emerging endophenotypes of depression: neuroticism, blunted reward learning and cognitive control deficits. Data were drawn from the ongoing multi-site EMBARC study. We estimated intracranial current density for standard EEG frequency bands in 82 unmedicated adults with MDD, using Low-Resolution Brain Electromagnetic Tomography (LORETA). Region-of- interest and whole-brain analyses tested associations between resting state EEG current density and endophenotypes of interest. Neuroticism was associated with increased resting gamma (36.5­44 Hz) current density in the ventral (subgenual) anterior cingulate cortex (ACC) and orbitofrontal cortex (OFC). In contrast, reduced cognitive control correlated with decreased gamma activity in the left dorsolateral prefrontal cortex (dlPFC), decreased theta (6.5­8 Hz) and alpha2 (10.5­12 Hz) activity in the dorsal ACC, and increased alpha2 activity in the right dlPFC. Finally, blunted reward learning correlated with lower OFC and left dlPFC gamma activity. Computational modeling of trial-by-trial reinforcement learning further indicated that lower OFC gamma activity was linked to reduced reward sensitivity. Three putative endophenotypes of depression were found to have partially dissociable resting intracranial EEG correlates, reflecting different underlying neural dysfunctions. Overall, these findings highlight the need to parse the heterogeneity of MDD by focusing on promising endophenotypes linked to specific pathophysiological abnormalities.
  • doi pdf Serotonin's many meanings elude simple theories
  • Dayan, P and Huys QJM
  • eLife (2015) 4:e07390
  • doi pdf Pavlovian-to-instrumental transfer effects in the nucleus accumbens relate to relapse in alcohol dependence
  • Garbusow M*, Schad D*, Sebold M, Friedel E, Bernhardt N, Koch SP, Steinacher B, Kathmann N, Geurts DE, Sommer C, Müller DK, Nebe S, Paul S, Wittchen H-U, Zimmermann US, Walter H, Smolka MN, Sterzer P, Rapp MA, Huys QJM*, Schlagenhauf F* and Heinz A*
  • Addiction Biology (2016) 21(3):719-31
  • In detoxified alcohol-dependent patients, alcohol-related stimuli can promote relapse. However, to date, the mechanisms by which contextual stimuli promote relapse have not been elucidated in detail. One hypothesis is that such contextual stimuli directly stimulate the motivation to drink via associated brain regions like the ventral striatum and thus promote alcohol seeking, intake and relapse. Pavlovian-to-Instrumental-Transfer (PIT) may be one of those behavioral phenomena contributing to relapse, capturing how Pavlovian conditioned (contextual) cues determine instrumental behavior (e.g. alcohol seeking and intake). We used a PIT paradigm during functional magnetic resonance imaging to examine the effects of classically conditioned Pavlovian stimuli on instrumental choices in n=31 detoxified patients diagnosed with alcohol dependence and n=24 healthy controls matched for age and gender. Patients were followed up over a period of 3 months. We observed that (1) there was a significant behavioral PIT effect for all participants, which was significantly more pronounced in alcohol-dependent patients; (2) PIT was significantly associated with blood oxygen level-dependent (BOLD) signals in the nucleus accumbens (NAcc) in subsequent relapsers only; and (3) PIT-related NAcc activation was associated with, and predictive of, critical outcomes (amount of alcohol intake and relapse during a 3 months follow-up period) in alcohol-dependent patients. These observations show for the first time that PIT-related BOLD signals, as a measure of the influence of Pavlovian cues on instrumental behavior, predict alcohol intake and relapse in alcohol dependence.
  • doi pdf Decision-theoretic psychiatry
  • Huys QJM, Guitart-Masip M, Dolan RJ and Dayan P
  • Clin Psychol Sci (2015) 3(3):400-421
  • Psychiatric disorders profoundly impair many aspects of decision-making. Poor choices have negative consequences in the moment, and also make it very hard to navigate complex social environments. Computational neuroscience provides normative, neurobiologically informed, descriptions of the components of decision-making which serve as a platform for a principled exploration of dysfunctions. Here, we identify and discuss three classes of failure-modes arising in these formalisms. They stem from abnormalities in the framing of problems or tasks, from the mechanisms of cognition used to solve the tasks, or from the historical data available from the environment.
  • doi pdf Failure modes of the will: From goals to habits to compulsions?
  • Huys QJM and Petzschner F
  • Am J Psychiatry (2015), 172(3):1-3
  • Good decisions reflect the past and improve the future. Trouble ensues when the rewards of the past conflict with the goals of the future. New goals often fail to override behaviors reinforced in the past, for instance, when patients with obsessive-compulsive disorder (OCD) try to stop the counting behavior that has been their main strategy to avoid aversive intrusions. [...]

    In this issue of the Journal, Gillan et al. (5) add to an impressive catalog of studies on the imbalance between habitual and goal-directed decisions in OCD.

  • doi pdf The interplay of approximate planning strategies
  • Huys QJM, Lally N, Faulkner P, Eshel N, Seifritz E, Gershman SJ, Dayan P and Roiser JP
  • PNAS (2015), 112(10):3098-3103
    Commentary by Daniel, Schuck & Niv
  • Humans routinely formulate plans in domains so complex that even the most powerful computers are taxed. To do so, they seem to avail themselves of many strategies and heuristics that efficiently simplify, approximate, and hierarchically decompose hard tasks into simpler subtasks. Theoretical and cognitive research has revealed several such strategies; however, little is known about their establishment, interaction, and efficiency. Here, we use model-based behavioral analysis to provide a detailed examination of the performance of human subjects in a moderately deep planning task. We find that subjects exploit the structure of the domain to establish subgoals in a way that achieves a nearly maximal reduction in the cost of computing values of choices, but then combine partial searches with greedy local steps to solve subtasks, and maladaptively prune the decision trees of subtasks in a reflexive manner upon encountering salient losses. Subjects come idiosyncratically to favor particular sequences of actions to achieve subgoals, creating novel complex actions or "options."
  • doi pdf Ventral Striatal Dopamine Reflects Behavioral and Neural Signatures of Model-Based Control during Sequential Decision Making
  • Deserno L, Huys QJM, Boehme R, Buchert R, Heinze HJ, Grace AA, Dolan RJ, Heinz A and Schlagenhauf F
  • PNAS (2015), 112(5):1595-600
  • Dual system theories suggest that behavioral control is parsed between a deliberative `model-based' and a more reflexive `model-free' system. A balance of control exerted by these systems is thought to be related to dopamine neurotransmission. However, in the absence of direct measures of human dopamine, it remains unknown whether this reflects a quantitative relation with dopamine either in the striatum or other brain areas. Using a sequential decision task performed during fMRI, combined with striatal measures of dopamine using [18F]DOPA PET, we show that higher presynaptic ventral striatal dopamine levels were associated with a behavioral bias towards more model-based control. Higher presynaptic dopamine in ventral striatum was associated with greater coding of model-based information in lateral prefrontal cortex and diminished coding of model-free prediction errors in ventral striatum. Thus, inter- individual variability in ventral striatal presynaptic dopamine reflects a balance in the behavioral expression and the neural signatures of model-free and model-based control. Our data provide a novel perspective on how alterations in presynaptic dopamine levels might be accompanied by a disruption of behavioral control as observed in aging or neuropsychiatric diseases such as schizophrenia and addiction.
  • doi pdf Depression: A Decision-Theoretic Analysis
  • Huys QJM, Daw ND and Dayan P
  • Annu Rev Neurosci (2015), 38:1-23
  • The manifold symptoms of depression are a common and often transient feature of healthy life that are likely to be adaptive in difficult circumstances. It is when these symptoms enter a seemingly self-propelling spiral that the maladaptive features of a disorder emerge. We examine this malignant transformation from the perspective of the computational neuroscience of decision making, investigating how dysfunction of the brain's mechanisms of evaluation might lie at its heart. We start by considering the behavioural implications of pessimistic evaluations of decision variables. We then provide a selective review of work suggesting how such pessimism might arise via specific failures of the mechanisms of evaluation or state estimation. Finally, we analyze ways that miscalibration between the subject and environment may be self-perpetuating. We employ the formal framework of Bayesian decision theory as a foundation for this study, showing how most of the problems arise from one of its broad algorithmic facets, namely model-based reasoning.
  • 2014

  • pdf Dualdiagnose Schizophrenie und Sucht
  • Conradi J, Holper L and Huys QJM
  • Pro Mente Sana (2014) 4:28-29
  • Menschen mit Schizophrenie weisen ein deutlich höheres Risiko auf als gesunde Menschen, im Laufe ihres Lebens eine Abhängigkeitserkrankung zu entwickeln. Bei PatientInnen, die beide Erkrankungen aufweisen, treten häufiger Rückfälle, motorische Medikamentennebenwirkungen und soziale Probleme auf.
  • doi pdf Processing speed enhances model-based over model-free reinforcement learning in the presence of high working memory functioning
  • Schad D, Jünger, Sebold M, Garbusow M, Bernhardt N, Javadi AH, Zimmermann US, Smolka M, Heinz A, Rapp MA* and Huys QJM*
  • Front. Psychol. (2014) 5:1450
  • Theories of decision-making and its neural substrates have long assumed the existence of two distinct and competing valuation systems, variously described as goal-directed versus habitual, or, more recently and based on statistical arguments, as model-free versus model-based reinforcement-learning. Though both have been shown to control choices, the cognitive abilities associated with these systems are under ongoing investigation. Here we examine the link to cognitive abilities, and find that individual differences in processing speed covary with a shift from model-free to model-based choice control in the presence of above-average working memory function. This suggests shared cognitive and neural processes; provides a bridge between literatures on intelligence and valuation; and may guide the development of process models of different valuation components. Furthermore, it provides a rationale for individual differences in the tendency to deploy valuation systems, which may be important for understanding the manifold neuropsychiatric diseases associated with malfunctions of valuation.
  • doi pdf Chronic alcohol intake abolishes the relationship between dopamine synthesis capacity and learning signals in ventral striatum
  • Deserno L, Beck A, Huys QJM, Lorenz RC, Buchert R, Buchholz HG, Plotkin M, Kumakura Y, Cumming P, Heinze HJ, Rapp MA and Heinz A
  • Eur J Neurosci (2014), 41(4):477-86
  • Drugs of abuse elicit dopamine release in ventral striatum, possibly biasing dopamine-driven reinforcement learning towards drug-related reward at the expense of non-drug-related reward. Indeed, reactivity in dopaminergic target areas of patients with alcohol dependence is shifted from non-drug-related stimuli towards drug-related stimuli. Such `hijacked' dopamine signals may impair flexible learning from non-drug-related rewards and thus promote craving for the drug of abuse. Here, we used fMRI to measure ventral striatal activation by reward prediction errors (RPEs) during a probabilistic reversal learning task in recently detoxified alcohol- dependent patients and healthy controls (N=27). The same subjects also underwent FDOPA PET to assess ventral striatal dopamine synthesis capacity. Neither ventral striatal activation by RPEs, nor striatal dopamine synthesis capacity differed between groups. However, ventral striatal coding of RPEs was negatively correlated with craving in patients. Furthermore, we found a negative correlation between ventral striatal coding of RPEs and dopamine synthesis capacity in healthy controls, but not in alcohol-dependent patients. Moderator analyses showed that the magnitude of the association between RPE coding and dopamine synthesis capacity depended on the amount of chronic-habitual alcohol intake. Given the relatively small sample size, a power analysis showed that it is rather unlikely to obtain these results by chance. Using a multimodal imaging approach, this study suggests that dopaminergic modulation of neural learning signals is disrupted in alcohol dependence and this is linked to long-term alcohol intake of patients. Alcohol intake may perpetuate itself by interfering with dopaminergic modulation of neural learning signals in ventral striatum, thus increasing craving for habitual drug intake.
  • doi pdf The effects of life stress and neural learning signals on fluid intelligence
  • Friedel E, Schlagenhauf F, Beck A, Dolan RJ, Huys QJM*, Rapp MA* and Heinz A*
  • Eur Arch Psychiatry Clin Neurosci (2014), 70(2):122-31
  • Fluid intelligence (fluid IQ), defined as the capacity for rapid problem solving and behavioral adaptation, is known to be modulated by learning and experience. Both stressful life events (SLES) and neural correlates of learning [specifically, a key mediator of adaptive learning in the brain, namely the ventral striatal representation of prediction errors (PE)] have been shown to be associated with individual differences in fluid IQ. Here, we examine the interaction between adaptive learning signals (using a well-characterized probabilistic reversal learning task in combination with fMRI) and SLES on fluid IQ measures. We find that the correlation between ventral striatal BOLD PE and fluid IQ, which we have previously reported, is quantitatively modulated by the amount of reported SLES. Thus, after experiencing adversity, basic neuronal learning signatures appear to align more closely with a general measure of flexible learning (fluid IQ), a finding complementing studies on the effects of acute stress on learning. The results suggest that an understanding of the neurobiological correlates of trait variables like fluid IQ needs to take socioemotional influences such as chronic stress into account.
  • doi pdf Individual differences in bodily freezing predict emotional biases in decision making
  • Ly V, Huys QJM, Stins JF, Roelofs K and Cools R
  • Front. Behav. Neurosci., (2014), 8:237
  • Instrumental decision making has long been argued to be vulnerable to emotional responses. Literature on multiple decision making systems suggests that this emotional biasing might reflect effects of a system that regulates innately specified, evolutionarily preprogrammed responses. To test this hypothesis directly, we investigated whether effects of emotional faces on instrumental action can be predicted by effects of emotional faces on bodily freezing, an innately specified response to aversive relative to appetitive cues. We tested 43 women using a novel emotional decision making task combined with posturography, which involves a force platform to detect small oscillations of the body to accurately quantify postural control in upright stance. On the platform, participants learned whole body approach-avoidance actions based on monetary feedback, while being primed by emotional faces (angry/happy). Our data evidence an emotional biasing of instrumental action. Thus, angry relative to happy faces slowed instrumental approach relative to avoidance responses. Critically, individual differences in this emotional biasing effect were predicted by individual differences in bodily freezing. This result suggests that emotional biasing of instrumental action involves interaction with a system that controls innately specified responses. Furthermore, our findings help bridge (animal and human) decision making and emotion research to advance our mechanistic understanding of decision making anomalies in daily encounters as well as in a wide range of psychopathology.
  • doi pdf Pavlovian-to-Instrumental Transfer in alcohol dependence - a pilot study
  • Garbusow M, Schad D, Sommer C, Jünger E, Sebold M, Friedel E, Wendt J, Kathmann N, Schlagenhauf F, Zimmerman U, Heinz A, Huys QJM*, Rapp MA*
  • Neuropsychobiology (2014) 70(2):111-21
  • Background: Pavlovian processes are thought to play an important role in the development, maintenance and relapse of alcohol dependence, possibly by influencing and usurping on- going thought and behavior. The influence of Pavlovian stimuli on on-going behavior is paradigmatically measured by Pavlovian-to-instrumental-transfer (PIT) tasks. These involve multiple stages and are complex. Whether increased PIT is involved in human alcohol dependence is uncertain. We therefore aimed to establish and validate a modified PIT paradigm that would be robust, consistent, and tolerated by healthy controls as well as by patients suffering from alcohol dependence, and to explore whether alcohol dependence is associated with enhanced Pavlovian-Instrumental transfer.
    Methods: 32 recently detoxified alcohol-dependent patients and 32 age and gender matched healthy controls performed a PIT task with instrumental go/no-go approach behaviours. The task involved both Pavlovian stimuli associated with monetary rewards and losses, and images of drinks.
    Results: Both patients and healthy controls showed a robust and temporally stable PIT effect. Strengths of PIT effects to drug-related and monetary conditioned stimuli were highly correlated. Patients more frequently showed a PIT effect and the effect was stronger in response to aversively conditioned CSs (conditioned suppression), but there was no group difference in response to appetitive CSs.
    Conclusion: The implementation of PIT has favorably robust properties in chronic alcohol- dependent patients and in healthy controls. It shows internal consistency between monetary and drug-related cues. The findings support an association of alcohol dependence with an increased propensity towards PIT.
  • doi pdf Model-based and model-free decisions in alcohol dependence
  • Sebold M, Deserno L, Nebe S, Schad DJ, Garbusow M, Hägele C, Keller J, Jünger E, Kathmann N, Smolka M, Rapp MA, Schlagenhauf F, Heinz A, Huys QJM
  • Neuropsychobiology (2014) 70(2):122-31
  • Background: Human and animal work suggests a shift from goal-directed to habitual decision-making in addiction. However, the evidence for this in human alcohol dependence is as yet inconclusive.
    Methods: Twenty-six healthy controls and twenty-six recently detoxified alcohol-dependent patients underwent behavioral testing with a two-step task designed to disentangle goal- directed and habitual response patterns.
    Results: Alcohol-dependent patients showed less evidence of goal-directed choices than healthy controls, particularly after losses. There was no difference in the strength of the habitual component. The group differences did not survive controlling for performance on the digit symbol substitution task.
    Conclusion: Chronic alcohol use appears to selectively impair goal-directed function, rather than promoting habitual responding. It appears to do so particularly after non-rewards, and this may be mediated by the effects of alcohol on more general cognitive functions subserved by the prefrontal cortex.
  • doi pdf Optimism as a prior belief about the probability of future reward
  • Stankevicius A*, Huys QJM*, Kalra A and Seriès P
  • PLoS Comp Biol (2014) 10(5): e1003605
  • Optimists hold positive a priori beliefs about the future. In Bayesian statistical theory, a priori beliefs can be overcome by experience. However, optimistic beliefs can at times appear surprisingly resistant to evidence, suggesting that optimism might also influence how new information is selected and learned. Here, we use a novel Pavlovian conditioning task, embedded in a normative framework, to directly assess how trait optimism, as classically measured using self-report questionnaires, influences choices between visual targets, as learning about their association with reward progresses. We find that trait optimism relates to an a priori belief about the likelihood of rewards, but not losses, in our task. Critically, this positive belief behaves like a probabilistic prior, i.e. its influence reduces with increasing experience. Contrary to findings in the literature related to unrealistic optimism and self-beliefs, it does not appear to influence the iterative learning process directly.
  • doi pdf Model-based and model-free decision-making
  • Huys QJM, Cruickshank A & Seriès P
  • In Jaeger, D and Jung, R (Ed): Encyclopædia of Computational Neuroscience. SpringerReference. Springer Verlag Berlin Heidelberg, 2014.
  • Reinforcement learning (RL) techniques are a set of solutions for optimal long-term action choice such that actions take into account both immediate and delayed consequences. They fall into two broad classes. Model-based approaches assume an explicit model of the environment and the agent. The model describes the consequences of actions and the associated returns. From this, optimal policies can be inferred. Psychologically, model-based descriptions apply to goal-directed decisions, in which choices reflect current preferences over outcomes. Model-free approaches forgo any explicit knowledge of the dynamics of the environment or the consequences of actions and evaluate how good actions are through trial-and-error learning. Model-free values underlie habitual and Pavlovian conditioned responses that are emitted reflexively when faced with certain stimuli. While model-based techniques have substantial computational demands, model-free techniques require extensive experience.
  • preprint Neurobiology and computational structure of decision-making in addiction
  • Huys QJM, Beck A, Dayan P and Heinz A
  • Mishara et al. (ed.): Phenomenological Neuropsychiatry: Bridging the clinic and clinical neuroscience
  • An increasing wealth of experimental detail is becoming available about the development and nature of addiction. Critical issues such as the varying vulnerabilities of individuals who develop addiction are being illuminated across levels of phenomenological, psychological and neurobiological detail. Furthermore, a rich theoretical understanding is emerging in the field of neural reinforcement learning, with glimmers as to how this might be related to the subjective experience of those individuals affected. In this chapter, we consider some particularly pressing current issues in the interface between experiment and theory, notably the so-called "compulsive" phase of drug taking.
  • doi pdf The role of learning-related dopamine signals in addiction vulnerability
  • Huys QJM, Tobler PT, Hasler G, Flagel SB
  • Progress in Neurobiology (2014): 211:31-77
  • Dopaminergic signals play a mathematically precise role in reward-related learning, and variations in dopaminergic signalling have been implicated in vulnerability to addiction. Here, we provide a detailed overview of the relationship between theoretical, mathematical and experimental accounts of phasic dopamine signalling, with implications for the role of learning-related dopamine signalling in addiction and related disorders. We describe the theoretical and behavioural characteristics of model-free learning based on errors in the prediction of reward, including step-by-step explanations of the underlying equations. We then use recent insights from an animal model that highlights individual variation in learning during a Pavlovian conditioning paradigm to describe overlapping aspects of incentive salience attribution and model-free learning. We argue that this provides a computationally coherent account of some features of addiction.
  • 2013

  • doi pdf
  • Striatal dysfunction during reversal learning in unmedicated schizophrenia patients
  • Schlagenhauf F*, Huys QJM*, Deserno M, Rapp MA, Beck A and Heinz A
  • Neuroimage (2014) 89:171-180
  • Subjects with schizophrenia are impaired at reinforcement-driven reversal learning from as early as their first episode. The neurobiological basis of this deficit is unknown. We obtained behavioral and fMRI data in 24 unmedicated, primarily first episode, schizophrenia patients and 24 age-, IQ- and gender-matched healthy controls during a reversal learning task. We supplemented our fMRI analysis, focusing on learning from prediction errors, with detailed computational modeling to probe task solving strategy including an ability to deploy an internal goal directed model of the task. Patients displayed reduced functional activation in the ventral striatum (VS) elicited by prediction errors. However, modeling task performance revealed that a subgroup did not adjust their behavior according to an accurate internal model of the task structure, and these were also the more severely psychotic patients. In patients who could adapt their behavior, as well as in controls, task solving was best described by cognitive strategies according to a Hidden Markov Model. When we compared patients and controls who acted according to this strategy, patients still displayed a significant reduction in VS activation elicited by informative errors that precede salient changes of behavior (reversals). Thus, our study shows that VS dysfunction in schizophrenia patients during reward-related reversal learning remains a core deficit even when controlling for task solving strategies. This result highlights VS dysfunction is tightly linked to a reward- related reversal learning deficit in early, unmedicated schizophrenia patients.
  • doi pdf
  • Serotonin and aversive Pavlovian control of instrumental behavior in humans
  • Geurts DEM, Huys QJM, Den Ouden HEM and Cools, R
  • J. Neuroscience (2013), 33(48): 18932-18939
  • Adaptive decision-making involves interaction between systems regulating Pavlovian and instrumental control of behavior. Here we investigate in humans the role of serotonin in such Pavlovian-instrumental transfer in both the aversive and the appetitive domain using acute tryptophan depletion, known to lower central serotonin levels. Acute tryptophan depletion attenuated the inhibiting effect of aversive Pavlovian cues on instrumental behavior, while leaving unaltered the activating effect of appetitive Pavlovian cues. These data suggest that serotonin is selectively involved in Pavlovian inhibition due to aversive expectations and have implications for our understanding of the mechanisms underlying a range of affective, impulsive, and aggressive neuropsychiatric disorders.
  • doi pdf Differential, but not opponent, effects of L-DOPA and citalopram on action learning with reward and punishment
  • Guitart-Masip M, Economides, Huys QJM, Frank MJ, Chowdhury R, Düzel E, Dayan P and Dolan RJ
  • Psychopharmacology (2013): 231:955-966.
  • Background: Decision-making involves two fundamental axes of control namely valence, spanning reward and punishment, and action, spanning invigoration and inhibition. We recently used a task whose contingencies explicitly decouple valence and action to show that these axes are inextricably coupled during learning. This results in a disadvantage in acquiring active choices in punished conditions and passive choices in rewarded conditions. The neuromodulators dopamine and serotonin are likely to play a role in these asymmetries. Dopamine signals anticipation of future rewards and is involved in an invigoration of motor responses leading to reward. Serotonin is associated with motor inhibition and punishment processing. Methods: Here we combined computational modelling with a pharmacological manipulation of dopamine and serotonin to examine acquisition of instrumental responding in a task that crosses action (go/no go) with valence (reward/punishment) in healthy human volunteers. Results: Contrary to expectation we found that levodopa decreased the coupling of action and valence that was evident in the placebo and citalopram groups. Citalopram had distinct effects and increased participants tendency to perform active responses independent of outcome valence, consistent with decreased motor inhibition. Conclusion: The current data highlights the importance of orthogonally manipulating action requirements and outcome valence if one wants to reveal the full complexity of the roles played by dopamine and serotonin in instrumental learning.
  • doi pdf Computational Psychiatry
  • Huys QJM
  • Encyclopædia of Computational Neuroscience (2014) 1-10
  • Computational Psychiatry is a heterogeneous field at the intersection of computational neuroscience and psychiatry. Incorporating methods from psychiatry, psychology, neuroscience, behavioural economics and machine learning, computational psychiatry focuses on building mathematical models of neural or cognitive phenomena relevant to psychiatric diseases. The models span a wide range - from biologically detailed models of neurons or networks to abstract models describing high-level cognitive abilities of an organism. Psychiatric diseases are conceptualized as either an extreme of normal function, or as a consequence of alterations in parts of the model.

    As in computational neuroscience more generally, the building of models forces key concepts to be made concrete and hidden assumptions to be made explicit. One critical functions of these models in the setting of psychiatry are their ability to bridge between low-level biological and high-level cognitive features. While many neurobiological alterations are known, the exclusively atheoretical focus of standard psychiatric nosology on high-level symptoms has as yet prevented an integration of these bodies of knowledge. David Marr pointed out that models at different levels may be independent (Marr, 1982). Nevertheless, implementational details may constrain functions at the computational level. The models used in computational psychiatry make these constraints explicit, and thereby aim to provide normative conduits between the different levels at which neural systems are analysed (Stephan et al., 2006; Huys et al., 2011; Hasler, 2012; Montague et al., 2012). This in turn allows for a principled approach to study dysfunctions, and indeed may allow the dysfunctions observed in psychiatry to inform neuroscience in general.

    Practically, it underpins hopes that computational techniques may facilitate the development of a psychiatric nomenclature based on an understanding of the underlying neuroscience. Computational models enhance experimental designs by allowing more intricate neural and/or cognitive processes to be inferred from complex features of the data, often via Bayesian inference. These aspects motivate hopes that it may facilitate the development of clinical treatment decision tools informed by advances in neuroscience.

  • doi pdf Pavlovian-Instrumental Transfer Effects in Alcohol Dependence
  • Genauck A, Huys QJM, Heinz AA and Rapp MA
  • Sucht (2013) 59(4):215-223
  • Hintergrund: Alkoholabhängigkeit ist eine Verkettung ungünstiger Entscheidungen in Bezug auf Alkoholkonsum. Dieses Entscheidungsmuster scheint sich u. a. wegen pawlowsch-instrumentellen Transfereffekten (PIT-Effekten) immer wieder zu reproduzieren. Ziel dieser Literaturzusammenschau ist, wichtige Befunde zum Zusammenhang zwischen PIT-Effekten und Suchterkrankungen zusammenzutragen und offene Fragen im Hinblick auf PIT bei Alkoholabhängigkeit aufzuzeigen. Methoden: Die Literaturzusammenschau nutzte keine systematische Literaturrecherche, sondern basierte auf den Recherchen im Rahmen der DFG Forschergruppe 1617 (Learning and Habitization in Alcohol Dependence, LeAD). Ergebnisse: PIT-Effekte könnten im Zusammenhang mit Alkoholabhängigkeit möglicherweise zu einem Teufelskreis führen. Dieser besteht aus der Verstärkung von PIT-Effekten durch Alkoholkonsum und verstärktem Alkoholkonsum aufgrund von verstärkten PIT-Effekten. Diskussion: PIT-Effekte bei Alkoholabhängigkeit sind bisher vorwiegend aus Tierstudien bekannt. Das PIT-Paradigma kann uns allerdings auch in der humanen Suchtforschung Aufschluss darüber geben, wie bestimmte Reizmuster Alkoholabhängige zum wiederholten Alkoholkonsum motivieren. Demnach können PIT-Experimente womöglich auch helfen Alkoholrückfälle vorherzusagen.

    Background: Disadvantageous decisions with respect to alcohol consumption play a central role in alcohol dependency (AD). This decision making pattern seems to be in part a result of Pavlovian-to-instrumental-transfer effects (PIT effects). The aim of this review is to summarize important findings on PIT within the scope of addiction disorders. Building on this, open questions in the field of human AD are discussed. Methods: This review is not based on a systematic and standardized literature research. Instead the review was based on the literature search conducted in DFG research group 1617 (Learning and Habitization in Alcohol Dependence, LeAD). Selection of research articles was based on expert opinion. Results: PIT effects in AD might possibly lead to a vicious cycle consisting of enhanced PIT effects through alcohol consumption and enhanced alcohol consumption through enhanced PIT effects. Discussion: PIT effects in alcohol addiction are mainly known from animal studies because there are but few human AD PIT studies. In human AD research the PIT paradigm may be able to reveal how particular cues disproportionally motivate AD patients to drink alcohol. PIT experiments thus have potential uses in the prediction of relapse and the measurement of addiction severity.

  • doi pdf
  • Aversive Pavlovian control of instrumental behaviour in humans
  • Geurts DEM*, Huys QJM*, Den Oouden HEM and Cools, R
  • J. Cogn. Neurosci. (2013) 25(9):1428-41
  • Adaptive behaviour involves interactions between systems regulating Pavlovian and instrumental control of actions. Here, we present the first investigation of the neural mechanisms underlying aversive Pavlovian-instrumental transfer using fMRI in humans. Recent evidence indicates that these Pavlovian influences on instrumental actions are action-specific: Instrumental approach is invigorated by appetitive Pavlovian cues, but inhibited by aversive Pavlovian cues. Conversely, instrumental withdrawal is inhibited by appetitive Pavlovian cues, but invigorated by aversive Pavlovian cues. We show that BOLD responses in the amygdala and the nucleus accumbens were associated with behavioural inhibition by aversive Pavlovian cues, irrespective of action context. Furthermore, BOLD responses in the ventromedial prefrontal cortex differed between approach and withdrawal actions. Aversive Pavlovian conditioned stimuli modulated connectivity between the ventromedial prefrontal cortex and the caudate nucleus. These results show that action-specific aversive control of instrumental behaviour involves the modulation of fronto-striatal interactions by Pavlovian conditioned stimuli.
  • doi pdf Mapping anhedonia onto reinforcement learning. A behavioural meta-analysis.
  • Huys QJM, Pizzagalli DA, Bogdan R and Dayan P
  • Biology of Mood & Anxiety Disorders (2013) 3:12
  • Background: Depression is characterised partly by blunted reactions to reward. However, tasks probing this deficiency have not distinguished insensitivity to reward from insensitivity to the prediction errors for reward that determine learning and are putatively reported by the phasic activity of dopamine neurons. We attempted to disentangle these factors with respect to anhedonia in the context of stress, Major Depressive Disorder (MDD), Bipolar Disorder (BPD) and a dopaminergic challenge.

    Methods: Six behavioural datasets involving 392 experimental sessions were subjected to a model-based, Bayesian meta-analysis. Participants across all six studies performed a probabilistic reward task that used an asymmetric reinforcement schedule to assess reward learning. Healthy controls were tested under baseline conditions, stress or after receiving the dopamine D2 agonist pramipexole. In addition, participants with current or past MDD or BPD were evaluated. Reinforcement learning models isolated the contributions of variation in reward sensitivity and learning rate.

    Results: MDD and anhedonia reduced reward sensitivity more than they affected the learning rate, while a low dose of the dopamine D2 agonist pramipexole showed the opposite pattern. Stress led to a pattern consistent with a mixed effect on reward sensitivity and learning rate.

    Conclusion: Reward-related learning reflected at least two partially separable contributions. The first related to phasic prediction error signalling, and was preferentially modulated by a low dose of the dopamine agonist pramipexole. The second related directly to reward sensitivity, and was preferentially reduced in MDD and anhedonia. Stress altered both components. Collectively, these findings highlight the contribution of model-based reinforcement learning meta-analysis for dissecting anhedonic behavior.

  • doi pdf Frontal theta overrides Pavlovian learning biases
  • Cavanagh J, Eisenberg M, Guitart-Masip M, Huys QJM and Frank MJ
  • J. Neurosci. (2013) 33(19):8541-8548
  • Pavlovian biases influence learning and decision making by intricately coupling reward seeking with action invigoration and punishment avoidance with action suppression. This bias is not always adaptive; it can oftentimes interfere with instrumental requirements. The prefrontal cortex is thought to help resolve such conflict between motivational systems, but the nature of this control process remains unknown. EEG recordings of mid-frontal theta band power are sensitive to conflict and predictive of adaptive control over behavior, but it is not clear whether this signal would reflect control over conflict between motivational systems. Here we utilized a task that orthogonalized action requirements and outcome valence while recording concurrent EEG in human participants. By applying a computational model of task performance, we derived parameters reflective of the latent influence of Pavlovian bias and how it was modulated by mid- frontal theta power during motivational conflict. Between subjects, individuals who performed better under Pavlovian conflict exhibited higher mid-frontal theta power. Within subjects, trial- to-trial variance in theta power was predictive of ability to overcome the influence of the Pavlovian bias, and this effect was most pronounced in individuals with higher mid-frontal theta to conflict. These findings demonstrate that mid-frontal theta is not only a sensitive index of prefrontal control, but it can also reflect the application of top-down control over instrumental processes.
  • pdf Learned helplessness and generalization
  • Lieder F, Goodman ND and Huys QJM
  • Proceedings of the 35th Annual Conference of the Cognitive Science Society (2013)
  • In learned helplessness experiments, subjects first experience a lack of control in one situation, and then show learning deficits when performing or learning another task in another situation. Generalization, thus, is at the core of the learned helplessness phenomenon. Substantial experimental and theoretical effort has been invested into establishing that a state- and task-independent belief about controllability is necessary. However, to what extent generalization is also sufficient to explain the transfer has not been examined. Here, we show qualitatively and quantitatively that Bayesian learning of action-outcome contingencies at three levels of abstraction is sufficient to account for the key features of learned helplessness, including escape deficits and impairment of appetitive learning after inescapable shocks.
  • doi pdf Dopamine restores reward prediction errors in older age
  • Chowdhury R, Guitart-Masip M, Lambert C, Dayan P, Huys QJ, Düzel E and Dolan RJ
  • Nature Neuroscience (2013) 16, 648-653
  • Senescence affects the ability to utilize information about the likelihood of rewards for optimal decision-making. Using functional magnetic resonance imaging in humans, we found that healthy older adults had an abnormal signature of expected value, resulting in an incomplete reward prediction error (RPE) signal in the nucleus accumbens, a brain region that receives rich input projections from substantia nigra/ventral tegmental area (SN/VTA) dopaminergic neurons. Structural connectivity between SN/VTA and striatum, measured by diffusion tensor imaging, was tightly coupled to inter-individual differences in the expression of this expected reward value signal. The dopamine precursor levodopa (L-DOPA) increased the task-based learning rate and task performance in some older adults to the level of young adults. This drug effect was linked to restoration of a canonical neural RPE. Our results identify a neurochemical signature underlying abnormal reward processing in older adults and indicate that this can be modulated by L-DOPA.
  • 2012

  • doi pdf Go and nogo learning in reward and punishment: Interactions between affect and effect
  • Guitart-Masip M*, Huys QJM*, Fuentemilla L, Dayan P, Düzel E and Dolan RJ
  • Neuroimage (2012) 62(1):154-66
  • Decision-making invokes two fundamental axes of control: affect or valence, spanning reward and punishment, and effect or action, spanning invigoration and inhibition. We studied the acquisition of instrumental responding in healthy human volunteers in a task in which we orthogonalized action requirements and outcome valence. Subjects were much more successful in learning active choices in rewarded conditions, and passive choices in punished conditions. Using computational reinforcement-learning models, we teased apart contributions from putatively instrumental and Pavlovian components in the generation of the observed asymmetry during learning. Moreover, using model-based fMRI, we showed that BOLD signals in striatum and substantia nigra/ventral tegmental area (SN/VTA) correlated with instrumentally learnt action values, but with opposite signs for go and no-go choices. Finally, we showed that successful instrumental learning depends on engagement of bilateral inferior frontal gyrus. Our behavioral and computational data showed that instrumental learning is contingent on overcoming inherent and plastic Pavlovian biases, while our neuronal data showed this learning is linked to unique patterns of brain activity in regions implicated in action and inhibition respectively.
  • doi pdf Bonsai trees in your head: How the Pavlovian system sculpts goal-directed choices by pruning decision trees
  • Huys QJM*, Eshel N*, O'Lions E, Sheridan L, Dayan P and Roiser JP
  • PLoS Comp Biol (2012) 8(3): e1002410
  • When planning a series of actions, it is usually infeasible to consider all potential future sequences; instead, one must prune the decision tree. Provably optimal pruning is, however, still computationally ruinous and the specific approximations humans employ remain unknown. We designed a new sequential reinforcement-based task and showed that human subjects adopted a simple pruning strategy: during mental evaluation of a sequence of choices, they curtailed any further evaluation of a sequence as soon as they encountered a large loss. This pruning strategy was Pavlovian: it was reflexively evoked by large losses and persisted even when overwhelmingly counterproductive. It was also evident above and beyond loss aversion. We found that the tendency towards Pavlovian pruning was selectively predicted by the degree to which subjects exhibited sub-clinical mood disturbance, in accordance with theories that ascribe Pavlovian behavioural inhibition, via serotonin, a role in mood disorders. We conclude that Pavlovian behavioural inhibition shapes highly flexible, goal- directed choices in a manner that may be important for theories of decision-making in mood disorders.
  • doi pdf Ventral striatal prediction error signalling is associated with dopamine synthesis capacity and fluid intelligence
  • Schlagenhauf F, Rapp MA, Huys QJM, Beck A, Wüstenberg T, Deserno L, Buchholz HG, Kalbitzer J, Buchert R, Kienast T, Cumming P, Plotkin M, Kumakura Y, Grace AA, Dolan RJ and Heinz A
  • Human Brain Mapping (2013) 34(6):1490-9
  • Fluid intelligence represents the capacity for flexible problem solving and rapid behavioral adaptation. Rewards drive flexible behavioral adaptation, in part via a teaching signal expressed as reward prediction errors in the ventral striatum, which has been associated with phasic dopamine release in animal studies. We examined a sample of 28 healthy male adults using multimodal imaging and biological parametric mapping with (1) functional magnetic resonance imaging during a reversal learning task and (2) in a subsample of 17 subjects also with positron emission tomography using 6-[(18) F]fluoro-L-DOPA to assess dopamine synthesis capacity. Fluid intelligence was measured using a battery of nine standard neuropsychological tests. Ventral striatal BOLD correlates of reward prediction errors were positively correlated with fluid intelligence and, in the right ventral striatum, also inversely correlated with dopamine synthesis capacity (FDOPA K#inapp). When exploring aspects of fluid intelligence, we observed that prediction error signaling correlates with complex attention and reasoning. These findings indicate that individual differences in the capacity for flexible problem solving relate to ventral striatal activation during reward-related learning, which in turn proved to be inversely associated with ventral striatal dopamine synthesis capacity.
  • 2011

  • doi pdf Action dominates valence in anticipatory representations in the human striatum and dopaminergic midbrain
  • Guitart-Masip M, Fuentemilla L, Bach DR, Huys QJM, Dayan P, Dolan RJ and Düzel D
  • J. Neurosci (2011) 31(21):7867-75
  • The acquisition of reward and the avoidance of punishment could logically be contingent on either emitting or withholding particular actions. However, the separate pathways in the striatum for go and no-go appear to violate this independence, instead coupling affect and effect. Respect for this interdependence has biased many studies of reward and punishment, so potential action-outcome valence interactions during anticipatory phases remain unexplored. In a functional magnetic resonance imaging study with healthy human volunteers, we manipulated subjects' requirement to emit or withhold an action independent from subsequent receipt of reward or avoidance of punishment. During anticipation, in the striatum and a lateral region within the substantia nigra/ventral tegmental area (SN/VTA), action representations dominated over valence representations. Moreover, we did not observe any representation associated with different state values through accumulation of outcomes, challenging a conventional and dominant association between these areas and state value representations. In contrast, a more medial sector of the SN/VTA responded preferentially to valence, with opposite signs depending on whether action was anticipated to be emitted or withheld. This dominant influence of action requires an enriched notion of opponency between reward and punishment.
  • doi pdf Disentangling the roles of approach, activation and valence in instrumental and Pavlovian responding
  • Huys QJM, Cools R, Gölzer M, Friedel E, Heinz A, Dolan RJ and Dayan P
  • PLoS Comp Biol (2011) 7(4): e1002028
  • Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to distinguish effects to do with value, specific actions, and general activation or inhibition. We show a high degree of sophistication in Pavlovian influences, with appetitive Pavlovian stimuli specifically promoting approach and inhibiting withdrawal, and aversive Pavlovian stimuli promoting withdrawal and inhibiting approach. These influences account for differences in the instrumental performance of approach and withdrawal behaviours. Finally, although losses are as informative as gains, we find that subjects neglect losses in their instrumental learning. Our findings argue for a view of the Pavlovian system as a constraint or prior, facilitating learning by alleviating computational costs that come with increased flexibility.
  • doi pdf Are computational models useful for psychiatry?
  • Huys QJM, Moutoussis M and Williams JW
  • Neural Networks (2011) 24(6):544-551
  • Mathematically rigorous descriptions of key hypotheses and theories are becoming more common in neuroscience and are beginning to be applied to psychiatry. In this article two fictional characters, Dr. Strong and Mr. Micawber, debate the use of such computational models (CMs) in psychiatry. We present four fundamental challenges to the use of CMs in psychiatry: (a) the applicability of mathematical approaches to core concepts in psychiatry such as subjective experiences, conflict and suffering; (b) whether psychiatry is mature enough to allow informative modelling; (c) whether theoretical techniques are powerful enough to approach psychiatric problems; and (d) the issue of communicating clinical concepts to theoreticians and vice versa. We argue that CMs have yet to influence psychiatric practice, but that they help psychiatric research in two fundamental ways: (a) to build better theories integrating psychiatry with neuroscience; and (b) to enforce explicit, global and efficient testing of hypotheses through more powerful analytical methods. CMs allow the complexity of a hypothesis to be rigorously weighed against the complexity of the data. The paper concludes with a discussion of the path ahead. It points to stumbling blocks, like the poor communication between theoretical and medical communities. But it also identifies areas in which the contributions of CMs will likely be pivotal, like an understanding of social influences in psychiatry, and of the co-morbidity structure of psychiatric diseases.
  • 2009

  • pdf Computational unhappiness: Modelling depression
  • Huys QJM and Dayan P
  • Frontiers in Neurosciences (2009) 3(2):263
  • doi pdf Serotonin in affective control
  • Dayan P and Huys QJM
  • Annu Rev Neurosci (2009) :: 32: 95-126
  • Serotonin is a neuromodulator that is extensively entangled in fundamental aspects of brain function and behavior. We present a computational view of its involvement in the control of appetitively and aversively motivated actions. We first describe a range of its effects in invertebrates, endowing specific structurally fixed networks with plasticity at multiple spatial and temporal scales. We then consider its rather widespread distribution in the mammalian brain. We argue that this is associated with a more unified representational and functional role in aversive processing that is amenable to computational analyses with the kinds of reinforcement learning techniques that have helped elucidate dopamine's role in appetitive behavior. Finally, we suggest that it is only a partial reflection of dopamine because of essential asymmetries between the natural statistics of rewards and punishments.
  • doi pdf A Bayesian formulation of behavioral control
  • Huys QJM and Dayan P
  • Cognition (2009) Special Issue
  • Helplessness, a belief that the world is not subject to behavioral control, has long been central to our understanding of depression, and has influenced cognitive theories, animal models and behavioral treatments. However, despite its importance, there is no fully accepted definition of helplessness or behavioral control in psychology or psychiatry, and the formal treatments in engineering appear to capture only limited aspects of the intuitive concepts. Here, we formalize controllability in terms of characteristics of prior distributions over affectively charged environments. We explore the relevance of this notion of control to reinforcement learning methods of optimising behavior in such environments and consider how apparently maladaptive beliefs can result from normative inference processes. These results are discussed with reference to depression and animal models thereof.
  • 2008

  • pdf Psychiatry: insights into depression through normative decision-making models
  • Huys QJM, Vogelstein JV and Dayan P
  • NIPS 2008
  • Decision making lies at the very heart of many psychiatric diseases. It is also a central theoretical concern in a wide variety of fields and has undergone detailed, in-depth, analyses. We take as an example Major Depressive Disorder (MDD), applying insights from a Bayesian reinforcement learning framework. We focus on anhedonia and helplessness. Helplessness-a core element in the conceptual- izations of MDD that has lead to major advances in its treatment, pharmacolog- ical and neurobiological understanding-is formalized as a simple prior over the outcome entropy of actions in uncertain environments. Anhedonia, which is an equally fundamental aspect of the disease, is related to the effective reward size. These formulations allow for the design of specific tasks to measure anhedonia and helplessness behaviorally. We show that these behavioral measures capture explicit, questionnaire-based cognitions. We also provide evidence that these tasks may allow classification of subjects into healthy and MDD groups based purely on a behavioural measure and avoiding any verbal reports.
  • doi pdf suppl Serotonin, behavioral inhibition and negative mood
  • Dayan P and Huys QJM
  • PLoS Computational Biology (2008) 4(2): e4
  • Pavlovian predictions of future aversive outcomes lead to behavioral inhibition, suppression, and withdrawal. There is considerable evidence for the involvement of serotonin in both the learning of these predictions and the inhibitory consequences that ensue, although less for a causal relationship between the two. In the context of a highly simplified model of chains of affectively charged thoughts, we interpret the combined effects of serotonin in terms of pruning a tree of possible decisions, (i.e., eliminating those choices that have low or negative expected outcomes). We show how a drop in behavioral inhibition, putatively resulting from an experimentally or psychiatrically influenced drop in serotonin, could result in unexpectedly large negative prediction errors and a significant aversive shift in reinforcement statistics. We suggest an interpretation of this finding that helps dissolve the apparent contradiction between the fact that inhibition of serotonin reuptake is the first-line treatment of depression, although serotonin itself is most strongly linked with aversive rather than appetitive outcomes and predictions.

PhD thesis

  • pdf Reinforcers and control: Towards a computational ætiology of depression
  • Huys QJM
  • Gatsby Computational Neuroscience Unit, UCL (2007)
  • Depression, like many psychiatric disorders, is a disorder of affect. Over the past decades, a large number of affective issues in depression have been characterised, both in human experiments and animal models of the disorder. Over the same period, experimental neuroscience, helped by computational theories such as reinforcement learning, has provided detailed descriptions of the psychology and neurobiology of affective decision making. Here, we attempt to harvest the advances in the understanding of the brain's normal dealings with rewards and punishments to dissect out and define more clearly the components that make up depression. We start by exploring changes to primary reinforcer sensitivity in the learned helplessness animal models of depression. Then, a detailed formalisation of control in a goal-directed decision making framework is presented and related to animal and human data. Finally, we show how serotonin's joint involvement in reporting negative values and inhibiting actions may explain some aspects of its involvement in depression. Throughout, aspects of depression are seen as emerging from normal affective function and reinforcement learning, and we thus conclude that computational descriptions of normal affective function provide one possible avenue by which to define an ætiology of depression.

Abstracts / Unrefereed

    2013

  • Habitual and goal-directed learning in alcohol dependence
  • M Sebold, M Garbusow, L Deserno, N Bernhard, A Genauck, C Hägele, E Friedel, C Sommer, D Schad, E Jünger, A Beck, U Zimmermann, M Rapp, F Schlagenhauf, M Smolka, A Heinz and QJM Huys,
  • Dopamine (2013)
  • Representations for planning
  • Z Kurth-Nelson, W Penny, QJM Huys, M Guitart-Masip, A Jafarpour, D Hassabis, G Barnes, P Dayan and RJ Dolan
  • Einstein Symposium Berlin (2013)
  • poster Controllability and resource-rational planning
  • Lieder F, Goodman ND and Huys QJM
  • Cosyne (2013)
  • 2012

  • Optimism can function as a normative prior belief on the probability of rewards
  • Stankevicius A, Huys QJM, Kalra A and Series P
  • Mathematical Biosciences Institute, Workshop on Cognitive Neuroscience, Columbus Ohio, USA, December 2012
  • 2011

  • poster The neural bases of reversal learning deficits in unmedicated schizophrenia patients
  • Schlagenhauf F, Huys QJM, Deserno M, Rapp MA, Beck A and Heinz A
  • Einstein meeting Berlin (2011)
  • Efficient decision-making is dependent on the efficient pruning of decision trees.
  • Faulkner P, Huys QJM, Eshel N, Dayan P and Roiser J
  • Summer Meeting of the British Association for Psychopharmacology (2011)
  • poster Psychopathy: a dysfunction in Pavlovian-to-instrumental transfer
  • Geurts D, von Borries K, Huys QJM, Verkes R-J and Cools R
  • Biological Psychiatry (2011)
  • 2010

  • doi Approaching avoidance: instrumental and Pavlovian asymmetries in the procesing of rewards and punishments
  • Huys QJM, Cools R, Gölzer M, Friedel E, Heinz A, Dolan RJ and Dayan P
  • Cosyne 2010
  • doi Bonsai trees: how the Pavlovian system sculpts sequential decisions
  • Huys QJM, Eshel N, Dayan P and Roiser JP
  • Cosyne 2010
  • 2008

  • Reward and helplessness in depression
  • Huys QJM, Bogdan R, den Ouden H, Lisanby SH, Pizzagalli DA and Dayan P
  • Cosyne 2008
  • 2007

  • Normative learning: a route to depression?
  • Huys QJM and Dayan P
  • CNS 2007
  • Depression as normal learning
  • Huys QJM and Dayan P
  • Nature medicine-roche conference on translational psychiatry
  • 2006

  • poster Optimal helplessness: a normative framework for depression
  • Huys QJM and Dayan P
  • CNS 2006
  • poster Malignant evaluation: reinforcement learning, neuromodulation and depression
  • Huys QJM and Dayan P
  • Cosyne 2006

Population coding

Peer reviewed

    2008

  • doi pdf
  • Encoding and decoding spikes for dynamic stimuli
  • Natarajan R, Huys QJM, Dayan P and Zemel R
  • Neural Computation (2008) 20(9):2325-60
  • Naturally occurring sensory stimuli are dynamic. In this letter, we consider how spiking neural populations might transmit information about continuous dynamic stimulus variables. The combination of simple encoders and temporal stimulus correlations leads to a code in which information is not readily available to downstream neurons. Here, we explore a complex encoder that is paired with a simple decoder that allows representation and manipulation of the dynamic information in neural systems. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner by a simple local learning rule.

  • 2007

  • doi pdf code Fast population coding
  • Huys QJM, Zemel R, Natarajan R and Dayan P
  • Neural Computation (2007) 19(2):460-97
  • Uncertainty coming from the noise in its neurons and the ill-posed nature of many tasks plagues neural computations. Maybe surprisingly, many studies show that the brain manipulates these forms of uncertainty in a probabilistically consistent and normative manner, and there is now a rich theoretical literature on the capabilities of populations of neurons to implement computations in the face of uncertainty. However, one major facet of uncertainty has received comparatively little attention: time. In a dynamic, rapidly changing world, data are only temporarily relevant. Here, we analyze the computational consequences of encoding stimulus trajectories in populations of neurons. For the most obvious, simple, instantaneous encoder, the correlations induced by natural, smooth stimuli engender a decoder that requires access to information that is nonlocal both in time and across neurons. This formally amounts to a ruinous representation. We show that there is an alternative encoder that is computationally and representationally powerful in which each spike con- tributes independent information; it is independently decodable, in other words. We suggest this as an appropriate foundation for understanding time-varying population codes. Furthermore, we show how adaptation to temporal stimulus statistics emerges directly from the demands of simple decoding.
  • 2004

  • pdf Probabilistic computation in spiking populations
  • Zemel R, Huys QJM, Natarajan R and Dayan P
  • NIPS 2004
  • As animals interact with their environments, they must constantly update estimates about their states. Bayesian models combine prior probabilities, a dynamical model and sensory evidence to update estimates optimally. These models are consistent with the results of many diverse psychophysical studies. However, little is known about the neural representation and manipulation of such Bayesian information, particularly in populations of spiking neurons. We consider this issue, suggesting a model based on standard neural architecture and activations. We illustrate the approach on a simple random walk example, and apply it to a sensorimotor integration task that provides a particularly compelling example of dynamic probabilistic computation.
  • Abstracts / Unrefereed

      2006

    • abstract talk Fast population coding
    • Huys QJM / Natarajan R / Zemel R/ Ernst M / Dayan P
    • CNS 2006
    • Dynamic population codes for sensorimotor processing
    • Natarajan R / Huys QJM / Dayan P / Zemel R
    • Cosyne 2006
    • 2005

    • poster Population coding in a fast-changing world
    • Huys QJM / Zemel R / Natarajan R / Dayan P
    • Cosyne 2005
    • Representational pursuit: population codes for dynamic environments
    • Zemel R / Natarajan R / Huys QJM / Dayan P
    • Cosyne 2005
    • poster A simple population code in a fast-changing world
    • Huys QJM / Zemel R / Natarajan R / Dayan P
    • Soc. Neurosci. Abstr. 2005

    Single cells

    Peer reviewed

      2009

    • doi pdf code movie
    • Smoothing of, and parameter estimation from, noisy biophysical recordings
    • Huys QJM, Paninski L
    • PLoS Computational Biology (2009) 5(5): e1000379
    • Biophysically detailed models of single cells are difficult to fit to real data. Recent advances in imaging techniques allow simultaneous access to various intracellular variables, and these data can be used to significantly facilitate the modelling task. These data, however, are noisy, and current approaches to building biophysically detailed models are not designed to deal with this. We extend previous techniques to take the noisy nature of the measurements into account. Sequential Monte Carlo (``particle filtering'') methods, in combination with a detailed biophysical description of a cell, are used for principled, model-based smoothing of noisy recording data. We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner. Biophysically important parameters of detailed models (such as channel densities, intercompartmental conductances, input resistances, and observation noise) are inferred automatically from noisy data via expectation-maximisation. Overall, we find that model-based smoothing is a powerful, robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters in the face of recording noise.
    • 2006

    • doi pdf code Efficient estimation of detailed single-neuron models
    • Huys QJM, Ahrens MB and Paninski L
    • J. Neurophysiol. (2006) 96: 872-890
    • Biophysically accurate multicompart-mental models of individual neurons have significantly advanced our understanding of the input­ output function of single cells. These models depend on a large number of parameters that are difficult to estimate. In practice, they are often hand-tuned to match measured physiological behaviors, thus raising questions of identifiability and interpretability. We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. Recent experimental advances, notably in voltage-sensitive imaging, motivate us to assume access to: i) the spatiotemporal voltage signal in the dendrite and ii) an approximate description of the channel kinetics of interest. We show here that, given i and ii, parameters 1­3 can be inferred simultaneously by nonnegative linear regression; that this optimization problem possesses a unique solution and is guaranteed to converge despite the large number of parameters and their complex nonlinear interaction; and that standard optimization algorithms efficiently reach this optimum with modest computational and data requirements. We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 104 parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels.
    • 2005

    • pdf Large-scale biophysical parameter estimation in single neurons via constrained linear regression
    • Ahrens MB, Huys QJM and Paninski L
    • NIPS 2005
    • Our understanding of the input-output function of single cells has been substantially advanced by biophysically accurate multi-compartmental models. The large number of parameters needing hand tuning in these models has, however, somewhat hampered their applicability and interpretability. Here we propose a simple and well-founded method for auto- matic estimation of many of these key parameters: 1) the spatial distribution of channel densities on the cell's membrane; 2) the spatiotemporal pattern of synaptic input; 3) the channels' reversal potentials; 4) the intercompartmental conductances; and 5) the noise level in each compart- ment. We assume experimental access to: a) the spatiotemporal voltage signal in the dendrite (or some contiguous subpart thereof, e.g. via voltage sensitive imaging techniques), b) an approximate kinetic description of the channels and synapses present in each compartment, and c) the morphology of the part of the neuron under investigation. The key observation is that, given data a)-c), all of the parameters 1)-4) may be si- multaneously inferred by a version of constrained linear regression; this regression, in turn, is efficiently solved using standard algorithms, without any "local minima" problems despite the large number of parameters and complex dynamics. The noise level 5) may also be estimated by standard techniques. We demonstrate the method's accuracy on several model datasets, and describe techniques for quantifying the uncertainty in our estimates.

    Abstracts / Unrefereed

      2005

    • Model-based smoothing of noisy, intermittent biophysical samples
    • Huys QJM, Paninski L
    • Cosyne 2007
    • 2006

    • abstract poster talk Model-based optimal interpolation and filtering for noisy, intermittent biophysical recordings
    • Huys QJM, Paninski L
    • Cns 2006
    • 2005

    • poster Estimating non-homogeneous channel densities and synaptic activity from spatiotemporal dendritic voltage recordings
    • Ahrens MB, Huys QJM and Paninski L
    • Cosyne 2005
    • poster Estimating non-homogeneous channel densities and synaptic activity from spatiotemporal dendritic voltage recordings
    • Ahrens MB, Huys QJM and Paninski L
    • SfN 2005

    Medicine

    Peer reviewed

      2007

    • doi pdf Screening patients with sensorineural hearing loss for a vestibular Schwannoma using a Bayesian classifier
    • Nouarei SAR, Huys QJM, Chatrath P, Powles J and Harcourt JP
    • J. Clin. Otolaryngology (2007) 32(4):248-54
    • Objectives: Selecting patients with asymmetrical sensorineural hearing loss for further investigation continues to pose clinical and medicolegal challenges, given the disparity between the number of symptomatic patients, and the low incidence of vestibular schwannoma as the underlying cause. We developed and validated a diagnostic model using a generalisation of neural networks, for detecting vestibular schwannomas from clinical and audiological data, and compared its performance with six previously published clinical and audiological decision- support screening protocols.

      Design: Probabilistic complex data classification using a neural network generalization.

      Settings: Tertiary referral lateral skull base and a computational neuroscience unit.

      Participants: Clinical and audiometric details of 129 patients with, and as many age and sex-matched patients without vestibular schwannomas, as determined with magnetic resonance imaging.

      Main outcome measures: The ability to diagnose a patient as having or not having vestibular schwannoma.

      Results: A Gaussian Process Ordinal Regression Classifier was trained and cross-validated to classify cases as `with' or `without' vestibular schwannoma, and its diagnostic performance was assessed using receiver operator characteristic plots. It proved possible to pre-select sensitivity and specificity, with an area under the curve of 0.8025. At 95% sensitivity, the trained system had a specificity of 56%, 30% better than audiological protocols with closest sensitivities. The sensitivities of previously-published audiological protocols ranged between 82-97%, and their specificities ranged between 15-61%.

      Discussion: The Gaussian Process Ordinal Regression Classifier increased the flexibility and specificity of the screening process for vestibular schwannoma when applied to a sample of matched patients with and without this condition. If applied prospectively, it could reduce the number of `normal' magnetic resonance (MR) scans by as much as 30% without reducing detection sensitivity. Performance can be further improved through incorporating additional data domains. Current findings need to be reproduced using a larger dataset.

    Abstracts / Unrefereed

      2012

    • Multiple endocrinopathies presenting simultaneously in the post-partum phase
    • Layne, Lewis, Agustsson, Huys QJM, Kariyawasam and Thomas
    • SfE National Clinical Cases 2012
    • 2007

    • Screening of patients with sensorineural hearing loss for a vestibular Schwannoma
    • Nouarei SAR, Huys QJM, Chatrath P, Powles J and Harcourt JP
    • R. Soc. Med. Section of Otology Abs. 2006