title: Computational modelling of behavioural differences in anxiety disorders
Creators: name: Ferrari, Filippo
Publisher: name: The University of Edinburgh
Year: 2025
Subjects: subject: Computational Psychiatry
subject: Anxiety
subject: Computational modelling

description: Anxiety is one of the most common types of mental disorders which manifests itself in different forms and intensities based on the types of situations inducing it and which can cause significant distress in people experiencing it. Despite the high prevalence of anxiety in the general population, a mechanistic understanding of anxiety disorders is still limited, with difficulties in providing precise diagnosis and predicting treatment outcomes due to its varied nature and high comorbidity with other disorders. In recent years, computational modelling has been increasingly used to formalise and investigate the mechanisms behind anxiety and other psychiatric disorders in an effort to provide precise, measurable descriptions of the key processes involved, ranging from the neural to the behavioural level. In this thesis, we investigate three areas of human behaviour known to be affected by anxiety: aversive learning, economic decision-making and estimation of uncertainty. The first study of this thesis investigates whether anxiety is linked to overgeneralisation of threat learning by modelling data collected in an avoidance learning task using Reinforcement Learning and Drift Diffusion models. A first experiment, previously conducted in the Seriès' Lab, found that anxious individuals overgeneralise threat learning due to impairments in discriminating between safe and aversive stimuli, poorer instrumental performance, higher perceived difficulty and diminished punishment sensitivity. In this thesis, we replicate this analysis on a second dataset collected using the same task on clinical participants diagnosed with Generalised Anxiety Disorder (GAD). We found that the original results did not replicate in this new dataset due to methodological differences with the initial design. The second study presented in this thesis addresses the impact of anxiety on individual propensities to risk and loss aversion in a gambling task delivered online. The experiment also investigates how individual levels of risk and loss aversion affected individual attitudes towards vaccines and decisions taken during the COVID pandemic, and whether these decisions were modulated by anxiety levels. Using a hierarchical Bayesian version of Prospect Theory models, we found increased loss aversion in individuals with higher levels of GAD and no differences in risk aversion. We also found no links between individual levels of risk and loss aversion and attitudes towards vaccines and COVID, possibly due to the economic decision-making task used in the study. The third study in this thesis examines how anxiety may affect estimation of uncertainty in the environment. When we interact with the environment, several types of uncertainty like expected and unexpected uncertainty affect our decisions. Each of these types of uncertainty have different, sometimes opposite, effects on how we learn from the environment. First, we report the results of a systematic review of the computational anxiety literature regarding estimation of uncertainty. We find that the literature agrees on anxiety being linked to an overly volatile view of the environment, which results in an impairment in adjusting learning as a result of changes in the environment. We also highlight current challenges and limitation due to the heterogeneity of tasks and computational models used in the literature, and we present a case study in which we compare two widely used families of computational models used in the literature, namely Reinforcement Learning models and the Hierarchical Gaussian Filter. Second, we design a set of experiments aimed at testing whether different uncertainty manipulations and the valence of the outcomes play a role in uncertainty processing and anxiety disorders. We find that, when moving from a more stable to a more volatile environment, individuals with high levels of anxiety, depression, PTSD and autistic traits show reduced adaptation of learning rates in punishing environments and increased adaptation of learning rates and greater reliance on past choices in rewarding environments. We find that intolerance of uncertainty mediates the effects of anxiety, depression, PTSD symptoms and autistic traits on punishment learning rate adaptation, while intolerance of uncertainty only mediates the effects of state anxiety, depression and PTSD symptoms on reward learning rate adaptation. We propose intolerance of uncertainty as a transdiagnostic factor that may capture differences in uncertainty processing across multiple disorders. We also discuss differences in results obtained through Reinforcement Learning models and Hierarchical Gaussian Filters and we discuss methodological challenges in using these models within a hierarchical Bayesian framework.