Autonomic Differentiation of Emotions: A Cluster Analysis Approach

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dc.contributor.authorStephens, Chad Louisen
dc.contributor.committeechairFriedman, Bruce H.en
dc.contributor.committeememberHarrison, David W.en
dc.contributor.committeememberCooper, Robin K. Pannetonen
dc.contributor.departmentPsychologyen
dc.date.accessioned2017-10-18T04:18:16Zen
dc.date.adate2007-10-16en
dc.date.available2017-10-18T04:18:16Zen
dc.date.issued2007-09-25en
dc.date.rdate2007-10-16en
dc.date.sdate2007-10-09en
dc.description.abstractThe autonomic specificity of emotion is intrinsic for many major theories of emotion. One of the goals of this study was to validate a standardized set of music clips to be used in studies of emotion and affect. This was accomplished using self-reported affective responses to 40 music pieces, noise, and silence clips in a sample of 71 college-aged individuals. Following the music selection phase of the study; the validated music clips as well as film clips previously shown to induce a wide array of emotional responses were presented to 50 college-aged subjects while a montage of autonomic variables were measured. Evidence for autonomic discrimination of emotion was found via pattern classification analysis replicating findings from previous research. It was theorized that groups of individuals could be identified based upon individual response specificity using cluster analytic techniques. Single cluster solutions for all emotion conditions indicated that stimulus response stereotypy of emotions was more powerful than individual patterns. Results from pattern classification analysis and cluster analysis support the concept of autonomic specificity of emotion.en
dc.description.degreeMaster of Scienceen
dc.description.notes[Appendix B: Beck Depression Inventory, p. 61-64, was removed Oct. 4, 2011 GMc]en
dc.identifier.otheretd-10092007-183519en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-10092007-183519/en
dc.identifier.urihttp://hdl.handle.net/10919/79690en
dc.language.isoen_USen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectPattern Classification Analysisen
dc.subjectAutonomic Specificityen
dc.subjectEmotionen
dc.titleAutonomic Differentiation of Emotions: A Cluster Analysis Approachen
dc.typeThesisen
dc.type.dcmitypeTexten
thesis.degree.disciplinePsychologyen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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