Autonomic Differentiation of Emotions: A Cluster Analysis Approach

by Stephens, Chad Louis

Abstract (Summary)
The 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.
Bibliographical Information:

Advisor:David W. Harrison; Robin Panneton; Bruce H. Friedman

School:Virginia Polytechnic Institute and State University

School Location:USA - Virginia

Source Type:Master's Thesis



Date of Publication:10/16/2007

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