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Soc Cogn Affect Neurosci. 2015 Nov;10(11):1437-48. doi: 10.1093/scan/nsv032. Epub 2015 Mar 25.

Multivariate neural biomarkers of emotional states are categorically distinct.

Author information

1
Department of Psychology & Neuroscience and Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.
2
Department of Psychology & Neuroscience and Center for Cognitive Neuroscience, Duke University, Durham, NC, USA klabar@duke.edu.

Abstract

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.

KEYWORDS:

Bayesian model comparison; affect; emotion; functional magnetic resonance imaging; multi-voxel pattern analysis; pattern classification

PMID:
25813790
PMCID:
PMC4631142
DOI:
10.1093/scan/nsv032
[Indexed for MEDLINE]
Free PMC Article

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