Anxiety disorders are the most common mental health disorders and comprise a large number of years lost to disability. The work in this thesis is oriented towards understanding anxiety using a computational approach, focusing on uncertainty estimation as a key process. Chapter 1 introduces the role of uncertainty within anxiety and motivates the subsequent experimental chapters. Chapter 2 is a review of the computational role of the amygdala in humans, a key area for uncertainty computation. Chapter 3 is an experimental chapter which aimed to address gaps in the literature highlighted in the preceding chapters, namely the link between sensory uncertainty processing and anxiety and the role of the amygdala in this process. This chapter focuses on the development of a novel computational hierarchical Bayesian model to quantify sensory uncertainty and its application to neuroimaging data, with intolerance of uncertainty relating to greater neural activation in the insula but not amygdala. Chapter 4 targets the computational mechanisms underlying the negative self-bias observed in subclinical social anxiety. Again, this chapter focuses on the development of novel computational belief-update models which explicitly model uncertainty. Here, we see that a reduced trait self-positivity underpins this negative social evaluation process. The final experimental chapter presented in Chapter 5 investigates the link between different computational mechanisms, such as uncertainty, and a range of mood and anxiety symptomatology. This study revealed cognitive, social and somatic computational profiles that share a threat bias mechanism but have distinct negative-self bias and aversive learning signatures. Contrary to expectations, none of the uncertainty measures showed any associations with anxiety symptom subtypes. Finally, chapter 6 brings together the work in this thesis and alongside limitations of the work, discusses how these experiments contribute to our understanding of anxiety and the role of uncertainty across the anxiety spectrum
