Computational Models of Anxiety: Nascent Efforts and Future Directions

Sharp, Paul B.; Eldar, Eran

doi:10.1177/0963721418818441

Cited by 28 publications

(29 citation statements)

References 44 publications

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“…learning rates – how quickly one might switch bandits following a punishment, or how long one persists in choosing a previously rewarded bandit). If altered response to uncertainty were a core feature of anxiety symptoms, we would predict that the mechanisms parameterised by reinforcement-learning models should differ in individuals with high levels of anxiety symptomatology11. Specifically, given that anxiety is associated with a bias towards aversive processing – i.e., negative affective bias 12–14- we might predict that anxiety will selectively increase the weights of aversive-specific parameters in reinforcement-learning algorithms: i.e., punishment sensitivity and punishment learning rate.…”

Section: Introductionmentioning

confidence: 99%

Altered learning under uncertainty in unmedicated mood and anxiety disorders

et al. 2019

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Anxiety is characterized by altered responses under uncertain conditions, but the precise mechanism by which uncertainty changes the behaviour of anxious individuals is unclear. Here we probe the computational basis of learning under uncertainty in healthy individuals and individuals with a mix of mood and anxiety disorders. Participants chose between four competing slot machines with fluctuating, reward/punishment outcomes during safety and stress. We predicted that anxious individuals under stress would learn faster about punishments, and exhibit choices that were more affected by them, formalising our predictions as parameters in reinforcement-learning accounts of behaviour. Overall, data suggest that anxious individuals are quicker to update their behaviour in response to negative outcomes (i.e. increased punishment learning-rates). When treating anxiety, it may therefore be more fruitful to encourage anxious individuals to integrate information over longer horizons when bad things happen, rather than try to blunt responses to negative outcomes.

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Section: Introductionmentioning

confidence: 99%

Altered learning under uncertainty in unmedicated mood and anxiety disorders

et al. 2019

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“…Recent studies have leveraged computational modelling to capture associations between learning processes and psychiatrically relevant dimensions in non-clinical samples [5][6][7][8] , as well as in clinical conditions ranging from anxiety and depression to psychosis [9][10][11][12] . A common finding across studies is that of altered learning rates, where psychopathology is linked to inappropriate weighting of evidence when updating value estimates 7,13,14 . Notably, there is evidence suggesting that people with clinically significant symptoms of anxiety and depression show biased learning as a function of the valence of information, updating faster in response to negative than positive outcomes presented as monetary losses and gains 12 , a bias that might engender a negative view of the environment.…”

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confidence: 99%

Associations between aversive learning processes and transdiagnostic psychiatric symptoms in a general population sample

Wise

Dolan

2020

Nat Commun

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Symptom expression in psychiatric conditions is often linked to altered threat perception, however how computational mechanisms that support aversive learning relate to specific psychiatric symptoms remains undetermined. We answer this question using an online game-based aversive learning task together with measures of common psychiatric symptoms in 400 subjects. We show that physiological symptoms of anxiety and a transdiagnostic compulsivity-related factor are associated with enhanced safety learning, as measured using a probabilistic computational model, while trait cognitive anxiety symptoms are associated with enhanced learning from danger. We use data-driven partial least squares regression to identify two separable components across behavioural and questionnaire data: one linking enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linking enhanced threat learning and heightened uncertainty estimation to symptoms of depression and social anxiety. Our findings implicate aversive learning processes in the expression of psychiatric symptoms that transcend diagnostic boundaries.

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“…Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK. 2 Wellcome Centre for Human Neuroimaging, University College London, London, UK. 3 University of California, Los Angeles, USA.…”

Section: Authors' Informationmentioning

confidence: 99%

“…Pursuing mechanistic models of psychopathology is a core component of an ongoing effort in the psychological sciences to transition from a descriptive to a causal science [1]. This approach seeks to mathematically formalize psychological functions and demonstrate how they are implemented in the human brain [2]. This approach, importantly, does not privilege biological explanations, as full mechanistic models must contain information processing accounts in order to fully understand the dynamics of the relevant biology [3,4].…”

Section: Introductionmentioning

confidence: 99%

Towards formal models of psychopathological traits that explain symptom trajectories

Sharp

Miller

Dolan

et al. 2020

BMC Med

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Background A dominant methodology in contemporary clinical neuroscience is the use of dimensional self-report questionnaires to measure features such as psychological traits (e.g., trait anxiety) and states (e.g., depressed mood). These dimensions are then mapped to biological measures and computational parameters. Researchers pursuing this approach tend to equate a symptom inventory score (plus noise) with some latent psychological trait. Main text We argue this approach implies weak, tacit, models of traits that provide fixed predictions of individual symptoms, and thus cannot account for symptom trajectories within individuals. This problem persists because (1) researchers are not familiarized with formal models that relate internal traits to within-subject symptom variation and (2) rely on an assumption that trait self-report inventories accurately indicate latent traits. To address these concerns, we offer a computational model of trait depression that demonstrates how parameters instantiating a given trait remain stable while manifest symptom expression varies predictably. We simulate patterns of mood variation from both the computational model and the standard self-report model and describe how to quantify the relative validity of each model using a Bayesian procedure. Conclusions Ultimately, we would urge a tempering of a reliance on self-report inventories and recommend a shift towards developing mechanistic trait models that can explain within-subject symptom dynamics.

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Computational Models of Anxiety: Nascent Efforts and Future Directions

Cited by 28 publications

References 44 publications

Altered learning under uncertainty in unmedicated mood and anxiety disorders

Altered learning under uncertainty in unmedicated mood and anxiety disorders

Associations between aversive learning processes and transdiagnostic psychiatric symptoms in a general population sample

Towards formal models of psychopathological traits that explain symptom trajectories

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