Advancing Emotion Theory with Multivariate Pattern Classification. (April 2014)

• Record Type: Journal Article
• Authors: Kragel, Philip A.; LaBar, Kevin S.
• Is Part Of: Emotion review. Volume 6:Number 2(2014:Apr.)
• Page Start: 160
• Page End: 174
• Publication Date: 2014-04
• Subjects: autonomic nervous system -- central nervous system -- emotion specificity -- model comparison -- multivariate pattern classification; Emotions -- Periodicals; Emotions -- Research -- Periodicals; 152.4
• DOI: 10.1177/1754073913512519
• Languages: English
• ISSNs: 1754-0739

Abstract

Characterizing how activity in the central and autonomic nervous systems corresponds to distinct emotional states is one of the central goals of affective neuroscience. Despite the ease with which individuals label their own experiences, identifying specific autonomic and neural markers of emotions remains a challenge. Here we explore how multivariate pattern classification approaches offer an advantageous framework for identifying emotion-specific biomarkers and for testing predictions of theoretical models of emotion. Based on initial studies using multivariate pattern classification, we suggest that central and autonomic nervous system activity can be reliably decoded into distinct emotional states. Finally, we consider future directions in applying pattern classification to understand the nature of emotion in the nervous system.