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Piasecki_WSU Thesis 2016.pdf (2.75 MB)

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Improving Anomaly Detection through Identification of Physiological Signatures of Unconscious Awareness

Piasecki, Alyssa Marie
http://orcid.org/0000-0002-4731-2322
http://rave.ohiolink.edu/etdc/view?acc_num=wright1461764365

Abstract Details

2016, Master of Science in Biomedical Engineering (MSBME), Wright State University, Biomedical Engineering.
Missed anomalies have the potential to cause detrimental effects in the Intelligence, Surveillance, and Reconnaissance (ISR) domain. One possible cause of these missed anomalies is that cognitive processing may not reach conscious awareness and may only be perceived by the unconscious mind. Identification of correlates of these unconscious processes could provide an insight into potential missed targets. The present study explored missed anomalies in a visual search task and the possibility of unconscious awareness. Eye metrics were recorded and a “Detection Threshold Model” was created and validated with a nominal logistic regression model, in order to characterize the search patterns and eye metrics of detection, non-detection, and possible unconscious detection. Results indicated that eye metrics of fixation count, fixation duration, mean saccade length, and backtrack rate predicted detections and non-detections with an overall accuracy of about 90%. Additionally, gaze plots of possible unconscious detections revealed signature search patterns of detection.
Mary Fendley, Ph.D. (Advisor)
Rik Warren, Ph.D. (Committee Member)
Nasser Kashou, Ph.D. (Committee Member)
81 p.
Biomedical Engineering; Biomedical Research; Engineering; Industrial Engineering; Physiological Psychology
Unconscious Detection; Eye-Tracking; Anomaly Response; Visual Search Task; Physiological Correlates

Recommended Citations

Citations

  • Piasecki, A. M. (2016). Improving Anomaly Detection through Identification of Physiological Signatures of Unconscious Awareness [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1461764365

    APA Style (7th edition)

  • Piasecki, Alyssa. Improving Anomaly Detection through Identification of Physiological Signatures of Unconscious Awareness. 2016. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1461764365.

    MLA Style (8th edition)

  • Piasecki, Alyssa. "Improving Anomaly Detection through Identification of Physiological Signatures of Unconscious Awareness." Master's thesis, Wright State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1461764365

    Chicago Manual of Style (17th edition)

wright1461764365
912
© 2016, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.