Harris, Kathryn Louise (2022) Generalising the Fringe-P3 Method for the Detection of Deception and Concealed Information Through Investigations into Stimuli and Analyses. Doctor of Philosophy (PhD) thesis, University of Kent,. (doi:10.22024/UniKent/01.02.94071) (KAR id:94071)
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Official URL: https://doi.org/10.22024/UniKent/01.02.94071 |
Resource title: | Breakthrough percepts of online identity: Detecting recognition of email addresses on the fringe of awareness |
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Resource type: | Publication |
DOI: | 10.1111/ejn.15098 |
KDR/KAR URL: | https://kar.kent.ac.uk/85599/ |
External URL: | http://dx.doi.org/10.1111/ejn.15098 |
Resource title: | Breakthrough percepts of famous names |
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Resource type: | Publication |
DOI: | 10.1016/j.cortex.2021.02.030 |
KDR/KAR URL: | https://kar.kent.ac.uk/94138/ |
External URL: | https://doi.org/10.1016/j.cortex.2021.02.030 |
Abstract
The Fringe-P3 method combined with a concealed information test is a counter-measure resistant method to detect (concealed) familiarity with stimuli (Alsufyani et al., 2019; Bowman et al., 2013, 2014). The Fringe-P3 method presents stimuli on the fringe of awareness through rapid serial visual presentation, where, typically, only familiar and salient stimuli can breakthrough into conscious awareness. When a salient stimulus breaks through into awareness, it generates a P3 brain response that can be detected through EEG. As such, if a familiar/salient probe stimulus (e.g., a famous name) breaks through into awareness and a P3 is detected, we can infer that the participant is familiar with that stimulus and, therefore, is concealing information about it. The Fringe-P3 concealed information test has been shown to detect familiarity with own-name and famous face stimuli at both the group and individual participants' level (Alsufyani et al., 2019; Bowman et al., 2013, 2014). Successful detection of familiarity at the individual participants' level is key, as the real world application of the method would be for individual suspects in forensic investigations to link them to crimes by detecting their familiarity with crime-related stimuli.
This thesis aimed to generalise the Fringe-P3 method by demonstrating that it can detect familiarity with a wider variety of stimuli beyond own-names and faces. Specifically, it aimed to demonstrate that the Fringe-P3 method can detect concealed information with familiar name, email address, and location image stimuli. In addition to using EEG to detect P3s in three experiments in this thesis, a fourth experiment used the attentional blink paradigm (where a participant's detection of a probe stimulus causes them to miss a target stimulus presented shortly after) as an alternative to EEG to detect concealed information. This thesis also proposed an alternative method of analysing EEG datasets to detect P3s using a template based on other participants' P3s, called the matched filter convolution analysis.
This thesis successfully provided proofs of concept that the Fringe-P3 method could be used to detect familiarity with famous name and email address stimuli at the group and individual participants' level. It also demonstrated potential to be used with location image stimuli and the attentional blink, following some methodological improvements suggested for future work. The matched filter convolution analysis successfully detected an equal number of P3s to the aggregated grand average of trials analysis that is currently used as the standard analysis for Fringe-P3 datasets.
Item Type: | Thesis (Doctor of Philosophy (PhD)) |
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Thesis advisor: | Bowman, Howard |
Thesis advisor: | Chu, Dominique |
DOI/Identification number: | 10.22024/UniKent/01.02.94071 |
Uncontrolled keywords: | Fringe-P3, lie detection, deception detection, EEG, electroencephalography, concealed information test, guilty knowledge test, rsvp, rapid serial visual presentation, attentional blink, P3, cybercrime, online identities, name recognition, location recognition |
Subjects: | Q Science > QA Mathematics (inc Computing science) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
SWORD Depositor: | System Moodle |
Depositing User: | System Moodle |
Date Deposited: | 19 Apr 2022 15:10 UTC |
Last Modified: | 06 Feb 2023 12:28 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/94071 (The current URI for this page, for reference purposes) |
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