The relationship between attention bias and anxiety has been robustly supported across paradigms and disorders; however, most published studies have ignored the known multidimensional nature of attention, and instead proceeded in measuring attention bias as a unitary construct, resulting in a lack of clarity regarding which attentional mechanisms contribute to specific manifestations of anxiety. In the current study we addressed this by collecting response latency data on three basic attentional processes, (1) attentional orienting, (2) attentional disengagement, and (3) attentional control to evaluate their relationship to specific anxiety symptoms. In a final sample of 149 college undergraduates, who either completed the computer tasks in-lab (N = 28) or online (N = 121), we used an unsupervised clustering approach (k-means clustering) to assign individual cases to clusters, depending upon their performance on measures of attention. We used a supervised machine learning approach (random forest), to cross-validate the unsupervised classification results. Anxiety symptoms were then set as predictors, predicting cluster membership using multinomial logistic regression. With the unsupervised k-means clustering approach, we found four clusters in the data. The random forest algorithm suggested variable prediction accuracy, dependent upon cluster size. Anxiety symptoms were unrelated to attention cluster membership. Study results were limited, which may be influenced by potential data collection and analytic factors. / Master of Science / Anxiety has been shown to be associated with enhanced attention for threatening information; however, most published studies have ignored the known multidimensional nature of attention, and instead proceeded in measuring attention as a unitary construct, resulting in a lack of clarity regarding which attentional mechanisms contribute to anxiety. In the current study we addressed this by collecting response latency data on three basic attentional processes: (1) attentional orienting for threatening information, (2) attentional disengagement from threatening infromation, and (3) attentional control to evaluate their relationship to specific anxiety symptoms. The final sample was 149 college undergraduates, who either completed the computer tasks in-lab (N = 28) or online (N = 121). We clustered individuals on these measures of attention (unsupervised k-means clustering). We used a supervised machine learning approach (random forest), to cross-validate the unsupervised classification results. Anxiety symptoms were then set as predictors, predicting cluster membership using multinomial logistic regression. We found four clusters of individuals in the data. The random forest algorithm suggested variable prediction accuracy, dependent upon cluster size. Anxiety symptoms were unrelated to attention cluster membership. Study results were limited, which may be influenced by potential data collection and analytic factors.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/78059 |
| Date | 19 January 2017 |
| Creators | Strege, Marlene Vernette |
| Contributors | Psychology, Richey, John A., Ollendick, Thomas H., Bell, Martha Ann, Gracanin, Denis |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Type | Thesis, Text |
| Format | application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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