Journal
EMOTION REVIEW
Volume 6, Issue 2, Pages 160-174Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/1754073913512519
Keywords
central nervous system; model comparison; autonomic nervous system; multivariate pattern classification; emotion specificity
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Funding
- NIDA NIH HHS [R01 DA027802, R01 DA014094] Funding Source: Medline
- NIMH NIH HHS [R21 MH098149] Funding Source: Medline
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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.
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