A Computational Approach to Understanding Maladaptive Cognition in Depression

CONTEXT

Depression is the single leading cause of disability worldwide and a major public health problem. Even with the best treatments, around 30% of patients remain unwell, demonstrating the importance of improving our understanding of depression. Decades of research in clinical psychology suggests that vulnerability to depression is associated with negative cognitive styles, such as attributing negative events to stable and global causes, often blaming oneself, and maladaptive metacognitive beliefs (about one's own cognitive processes), such as low self-confidence.

These biases are a focus of psychological therapies such as cognitive behavioural therapy (CBT), but the assessment of maladaptive depressive cognition is limited by imprecise measurement, relying on introspection and self-report. AIMS AND OBJECTIVES

This project aims to improve our understanding of the maladaptive cognitions driving depressive symptoms. To gain a more mechanistic understanding of the neurocognitive bases of (mal)adaptive cognition, we will leverage computational models of behaviour. This overarching goal will be achieved by conceptualising maladaptive depressive cognition as maladaptive attributions. To test this we will measure:

(a) biases in the attribution of positive and negative events to the self vs. external causes;

(b) biases in the metacognitive evaluations of decision confidence and their potential misattribution to action-outcome learning.

Using cutting-edge analysis methods, across online, clinical, and neuroimaging studies, this project will achieve the following objectives:

1. Clarify the neurocognitive mechanisms underlying adaptive attribution (of external events and of metacognitive signals), in healthy participants. 2. Identify behavioural markers of maladaptive attribution related to depressive symptoms in a non-clinical sample.

3. Test the specificity of markers of maladaptive attribution to depressive symptoms, relative to other common mental health problems. 4. Test the clinical relevance of markers of maladaptive attribution. POTENTIAL APPLICATIONS AND BENEFITS

Improving our understanding of the mechanisms that drive maladaptive cognition in depression, and underpin attributional processes in healthy participants, will constitute an important scientific contribution to the fields of clinical psychology and cognitive and computational neuroscience. Given the high societal cost of depression, this research is of high societal and clinical relevance.

Disseminating our findings to the wider society will demonstrate how a better understanding of basic cognitive processes may translate to understanding everyday behaviour. Presenting our project and findings to people with mental health problems, including service users, will allow receiving their feedback on our experimental designs and findings, and help broaden the perspective for future research.

The work will also be regularly disseminated to academic audiences, through publications and conferences, across the fields of psychology, neuroscience, and mental health. Engaging with clinical experts, by organising an interdisciplinary workshop, will help increase our clinical impact, establish novel collaborations, and receive expert feedback. Identifying behavioural and neural markers related to maladaptive cognition in depression offers a unique opportunity to develop novel tools that may subsequently help to refine differential diagnosis and improve treatment selection, as well as provide a foundation for the development of novel psychological interventions.