Decoding the Nature of Emotion in the Brain. Issue 6 (June 2016)

• Record Type: Journal Article
• Authors: Kragel, Philip A.; LaBar, Kevin S.
• Is Part Of: Trends in cognitive sciences. Volume 20:Issue 6(2016)
• Page Start: 444
• Page End: 455
• Publication Date: 2016-06
• Subjects: emotion -- affect -- valence -- fMRI -- multivariate pattern analysis -- neural decoding; Cognitive science -- Periodicals; Cognitive neuroscience -- Periodicals; 153.05
• DOI: 10.1016/j.tics.2016.03.011
• Languages: English
• ISSNs: 1364-6613

Abstract

Abstract : A central, unresolved problem in affective neuroscience is understanding how emotions are represented in nervous system activity. After prior localization approaches largely failed, researchers began applying multivariate statistical tools to reconceptualize how emotion constructs might be embedded in large-scale brain networks. Findings from pattern analyses of neuroimaging data show that affective dimensions and emotion categories are uniquely represented in the activity of distributed neural systems that span cortical and subcortical regions. Results from multiple-category decoding studies are incompatible with theories postulating that specific emotions emerge from the neural coding of valence and arousal. This 'new look' into emotion representation promises to improve and reformulate neurobiological models of affect. Trends: Due to limitations of univariate approaches, scientists have begun to apply multivariate statistical tools to decode how emotion constructs are represented in high-dimensional patterns of human brain activity. Recent studies show that functional neuroimaging data can be accurately classified along affective dimensions and discrete emotion categories. Data from studies classifying brain states into multiple emotion categories suggest that dimensions of valence and arousal do not principally organize neural representations of specific emotions.