Multivariate neural biomarkers of emotional states are categorically distinct.

Published

Journal Article

Understanding how emotions are represented neurally is a central aim of affective neuroscience. Despite decades of neuroimaging efforts addressing this question, it remains unclear whether emotions are represented as distinct entities, as predicted by categorical theories, or are constructed from a smaller set of underlying factors, as predicted by dimensional accounts. Here, we capitalize on multivariate statistical approaches and computational modeling to directly evaluate these theoretical perspectives. We elicited discrete emotional states using music and films during functional magnetic resonance imaging scanning. Distinct patterns of neural activation predicted the emotion category of stimuli and tracked subjective experience. Bayesian model comparison revealed that combining dimensional and categorical models of emotion best characterized the information content of activation patterns. Surprisingly, categorical and dimensional aspects of emotion experience captured unique and opposing sources of neural information. These results indicate that diverse emotional states are poorly differentiated by simple models of valence and arousal, and that activity within separable neural systems can be mapped to unique emotion categories.

Full Text

Duke Authors

Cited Authors

  • Kragel, PA; LaBar, KS

Published Date

  • November 2015

Published In

Volume / Issue

  • 10 / 11

Start / End Page

  • 1437 - 1448

PubMed ID

  • 25813790

Pubmed Central ID

  • 25813790

Electronic International Standard Serial Number (EISSN)

  • 1749-5024

International Standard Serial Number (ISSN)

  • 1749-5016

Digital Object Identifier (DOI)

  • 10.1093/scan/nsv032

Language

  • eng