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dc.contributor.advisorNunamaker, Jr., Jay F.en_US
dc.contributor.advisorBurgoon, Judee K.en_US
dc.contributor.authorMeservy, Thomas Oliver
dc.creatorMeservy, Thomas Oliveren_US
dc.date.accessioned2011-12-05T22:15:58Z
dc.date.available2011-12-05T22:15:58Z
dc.date.issued2007en_US
dc.identifier.urihttp://hdl.handle.net/10150/194056
dc.description.abstractHumans have long sought to use technology to augment human abilities and intellect. However, technology is traditionally employed only to create speedier solutions or more-rapid comprehension. A more challenging endeavor is to enable humans with technology to gain additional or enhanced comprehension that may not be possible to acquire otherwise. One such application is the use of technology to augment human abilities in detecting deception using nonverbal cues. Detecting deception is often critical, whether an individual is communicating with a close friend, negotiating a business deal, or screening individuals at a security checkpoint.The detection of deception is a challenging endeavor. A variety of studies have shown that humans have a hard time accurately discriminating deception from truth, and only do so slightly better than chance. Several deception detection methods exist; however, most of these are invasive and require a controlled environment.This dissertation presents a technological approach to detecting deception based on kinesic (i.e., movement-based) and vocalic (i.e., sounds associated with the voice) cues that is firmly grounded in deception theory and past empirical studies. This noninvasive approach overcomes some of the weaknesses of other deception detection methods as it can be used in a natural environment without cooperation from the individual of interest.The automatable approach demonstrates potential for increasing humans' ability to correctly identify those who display behaviors indicative of deception. The approach was evaluated using experimental and field data. The results of repeated measures analysis of variance, linear regression and discriminant function analysis suggest that the use of such a system could augment human abilities in detecting deception by as much as 15-25%. While there are a number of technical challenges that need to be addressed before such a system could be deployed in the field, there are numerous environments where it would be potentially useful.
dc.language.isoENen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectdeceptionen_US
dc.subjectkinesicsen_US
dc.subjectvocalicsen_US
dc.subjectdeception detectionen_US
dc.subjectautomatic extractionen_US
dc.titleAugmenting Human Intellect: Automatic Recognition of Nonverbal Behavior with Application in Deception Detectionen_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.contributor.chairNunamaker, Jr., Jay F.en_US
dc.contributor.chairBurgoon, Judee K.en_US
dc.identifier.oclc659747350en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberFong, Sandiwayen_US
dc.contributor.committeememberKruse, W. Johnen_US
dc.identifier.proquest2155en_US
thesis.degree.disciplineManagementen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePhDen_US
refterms.dateFOA2018-06-23T02:51:13Z
html.description.abstractHumans have long sought to use technology to augment human abilities and intellect. However, technology is traditionally employed only to create speedier solutions or more-rapid comprehension. A more challenging endeavor is to enable humans with technology to gain additional or enhanced comprehension that may not be possible to acquire otherwise. One such application is the use of technology to augment human abilities in detecting deception using nonverbal cues. Detecting deception is often critical, whether an individual is communicating with a close friend, negotiating a business deal, or screening individuals at a security checkpoint.The detection of deception is a challenging endeavor. A variety of studies have shown that humans have a hard time accurately discriminating deception from truth, and only do so slightly better than chance. Several deception detection methods exist; however, most of these are invasive and require a controlled environment.This dissertation presents a technological approach to detecting deception based on kinesic (i.e., movement-based) and vocalic (i.e., sounds associated with the voice) cues that is firmly grounded in deception theory and past empirical studies. This noninvasive approach overcomes some of the weaknesses of other deception detection methods as it can be used in a natural environment without cooperation from the individual of interest.The automatable approach demonstrates potential for increasing humans' ability to correctly identify those who display behaviors indicative of deception. The approach was evaluated using experimental and field data. The results of repeated measures analysis of variance, linear regression and discriminant function analysis suggest that the use of such a system could augment human abilities in detecting deception by as much as 15-25%. While there are a number of technical challenges that need to be addressed before such a system could be deployed in the field, there are numerous environments where it would be potentially useful.


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