The Default Mode Network's Role in Discrete Emotion. Issue 10 (October 2019)

• Record Type: Journal Article
• Authors: Satpute, Ajay B.; Lindquist, Kristen A.
• Is Part Of: Trends in cognitive sciences. Volume 23:Issue 10(2019)
• Page Start: 851
• Page End: 864
• Publication Date: 2019-10
• Subjects: emotion -- abstraction -- conceptualization -- memory -- default mode network -- multivoxel pattern analysis; Cognitive science -- Periodicals; Cognitive neuroscience -- Periodicals; 153.05
• DOI: 10.1016/j.tics.2019.07.003
• Languages: English
• ISSNs: 1364-6613

Abstract

Abstract : Emotions are often assumed to manifest in subcortical limbic and brainstem structures. While these areas are clearly important for representing affect (e.g., valence and arousal), we propose that the default mode network (DMN) is additionally important for constructing discrete emotional experiences (of anger, fear, disgust, etc.). Findings from neuroimaging studies, invasive electrical stimulation studies, and lesion studies support this proposal. Importantly, our framework builds on a constructionist theory of emotion to explain how instances involving diverse physiological and behavioral patterns can be conceptualized as belonging to the same emotion category. We argue that this ability requires abstraction (from concrete features to broad mental categories), which the DMN is well positioned to support, and we make novel predictions from our proposed framework. Highlights: Emotions involve the coordinated activity of large-scale functional networks. The DMN has been linked with emotion but its mechanistic role remains unclear. Most prior accounts link DMN to emotion given the role of ventromedial prefrontal cortex (VMPFC) in more general affective processing (valence and arousal) or the broader DMN in generating internal states. Data from human neuroimaging, electrical stimulation, and lesion studies suggest that the DMN is implicated in representing discrete experiences of emotion (fear, anger, sadness etc.). The DMN also involves the usage of prior experience and knowledge to guide information processing to support abstraction and granularity. A theoretical framework is presented for how emotions involve representations at multiple levels of abstraction, and also how representations of discrete emotions vary in terms of their granularity.