Multivariate Discrimination of Emotion-Specific Autonomic Nervous System Activity

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Virginia Tech

The present study investigated autonomic nervous system (ANS) patterning during experimentally manipulated emotion. Film clips previously shown to induce amusement, anger, contentment, disgust, fear, and sadness, in addition to a neutral control, were presented to 34 college-aged subjects while electrodermal activity, blood pressure and electrocardiogram (ECG) were recorded as was self-reported affect. Mean and mean successive difference of inter-beat interval were derived from the ECG. Pattern classification analysis revealed emotion-specific patterning for all emotion conditions except disgust. Discriminant function analysis was used to describe the location of discrete emotions within a dimensional affective state space, for both self-report and ANS activity. Findings suggest traditional dimensional emotion models accurately describe the state space for self-reported emotion, but may require modification in order to accurately describe the state space for ANS activity during discrete emotions. Proposed modifications are consistent with the adoption of a discrete-dimensional hybrid model as well as current trends in emotion theory.

Emotion, Autonomic Specificity, Multivariate Pattern Classification