Identifying adaptative and maladaptive processes in performance anxiety.

Performance anxiety (PA), affecting 20-75% of professionals and students in fields like public speaking, sports, and performing arts, can significantly impact health and career development. Individuals with PA exhibit alterations in cardiorespiratory, cognitive and emotional states before and during a performance. These changes do not uniformly lead to negative outcomes, as some individuals excel under anxiety, while others falter.

Yet, despite its prevalence, the mechanisms by which PA influences performance -either impairing or facilitating it- remain largely unexplored. Adopting an interdisciplinary approach, this PhD project aims to identify the behavioural, cognitive, and neurophysiological processes contributing to facilitatory and detrimental effects of PA on performance.

Utilising robust Bayesian statistical techniques and expanding mathematical models of learning to performance settings, the project outcomes will inform strategies for reducing the adverse effects of PA, while bolstering resilience and well-being among those affected.

This proposal investigates the association between PA in skilled performers and the expression of negative learning biases -a heightened focus on negative feedback during learning. The project's focus on learning biases in PA is motivated by a significant body of clinical and computational research associating anxiety disorders with altered learning processes.

Despite this evidence, the lack of research into how learning biases shaped by PA affect performance is striking, largely resulting from limited interdisciplinary collaboration and method integration.

In this context, the PhD project seeks to address critical unresolved questions regarding the role of cognitive (e.g., worry) and physiological (e.g., increase of heart rate) components of PA in modulating responses to positive and negative feedback and associated performance outcomes. Sensitive cardiovascular measures will be used to identify facilitatory and detrimental effects of PA on performance.

In addition, measures of neural activity through electroencephalography (EEG) will be employed to gain insights into the cognitive underpinnings of feedback processing during performance in PA.

Central to this proposal is the novel application and integration of advanced methodologies in PA research. To identify factors modulating performance in PA, the project will employ robust Bayesian regression models for identifying associations between variables while avoiding confounds. I will capture realistic performance data as pianists learn from different forms of feedback, and record EEG and cardiovascular signals.

I plan to extend current mathematical models of learning used in psychiatry to explain realistic performance data in pianists. These models will shed light on the computational mechanisms underlying learning and adaptation during performance, such as how individuals infer the elements of performance contributing to success and failure, and how they adapt their beliefs dynamically when processing feedback