1. Examining the relationship between symptoms of depression and the Self-prioritization effect
- Author
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Pilling, Michael, Singh, Harpreet, Kumar, Sanjay, and Veldhuis, Alfred
- Subjects
Cognitive ,ANOVA ,Psychometrics ,Depression ,Social and Behavioral Sciences ,ML ,self cognition ,self processing ,Regression ,Machine Learning ,Association ,FOS: Psychology ,Experiment ,Association learning ,Online experiment ,self ,Artificial Intelligence ,AI ,Cognitive psychology ,Psychology ,Online ,BDI ,Psychopy ,self-referential processing - Abstract
The central purpose of this research is to clarify the relationship between depression and self-attention. The value in answering this question is that it has significant implications on how specific symptoms of depression could be treated psychologically and where in the brain there may be disordered functioning. The neurocognitive literature exploring ‘self in depression’ has produced contradictory and inconclusive findings in the exploration of the relationship between depression and self-attention. This may be due to research classifying participants as ‘depressed’ with very little attention to the specific depressive symptoms and therefore disorder subtypes. Research on non-clinical populations consistently shows that people have an attentional bias to information about themselves. This is the case even if this information is linked to them tenuously; for example, an arbitrary geometric shape associated with them. Studies have found results indicating that people with depression have higher than average attention to self, lower than average attention to self and no difference from controls. By using detailed psychometric questionnaires in tandem with an experimental design, this project can provide novel and useful insights into how depressive symptoms are related to self-attention bias. One of the ways to evaluate the specific symptomatic expression of depression is to use an index called Beck’s depression inventory (BDI-II). The scores on the BDI-II form two distinct clusters: cognitive and somatic affective. These two clusters have been robustly validated as accurately representing these types of symptoms by several meta-analyses. Gaining insight into the relationship between self-related attention and symptom clusters can help indicate if, for example, aberrant self-attention is predominantly a maladaptive internal self-representation (somatic) or, ‘self’ maladaptively construe in the emotional domain (cognitive). In addition to clarifying this relationship, this research will also provide insight into what aspect of the ‘self’ people with depression focus on (good aspects/bad aspects). The relationship between psychological characteristics and self-referential processing will be analysed using statistical analysis. This project will, however, further implement the use of Machine Learning (ML) to explore the utility of the experimental data in predicting symptoms as a potential diagnostic tool.
- Published
- 2023
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