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NEVER forget: negative emotional valence enhances recapitulation

Overview of attention for article published in Psychonomic Bulletin & Review, July 2017
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Title
NEVER forget: negative emotional valence enhances recapitulation
Published in
Psychonomic Bulletin & Review, July 2017
DOI 10.3758/s13423-017-1313-9
Pubmed ID
Authors

Holly J. Bowen, Sarah M. Kark, Elizabeth A. Kensinger

Abstract

A hallmark feature of episodic memory is that of "mental time travel," whereby an individual feels they have returned to a prior moment in time. Cognitive and behavioral neuroscience methods have revealed a neurobiological counterpart: Successful retrieval often is associated with reactivation of a prior brain state. We review the emerging literature on memory reactivation and recapitulation, and we describe evidence for the effects of emotion on these processes. Based on this review, we propose a new model: Negative Emotional Valence Enhances Recapitulation (NEVER). This model diverges from existing models of emotional memory in three key ways. First, it underscores the effects of emotion during retrieval. Second, it stresses the importance of sensory processing to emotional memory. Third, it emphasizes how emotional valence - whether an event is negative or positive - affects the way that information is remembered. The model specifically proposes that, as compared to positive events, negative events both trigger increased encoding of sensory detail and elicit a closer resemblance between the sensory encoding signature and the sensory retrieval signature. The model also proposes that negative valence enhances the reactivation and storage of sensory details over offline periods, leading to a greater divergence between the sensory recapitulation of negative and positive memories over time. Importantly, the model proposes that these valence-based differences occur even when events are equated for arousal, thus rendering an exclusively arousal-based theory of emotional memory insufficient. We conclude by discussing implications of the model and suggesting directions for future research to test the tenets of the model.

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Geographical breakdown

Country Count As %
Unknown 208 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 15%
Student > Bachelor 31 15%
Student > Master 30 14%
Researcher 22 11%
Student > Doctoral Student 10 5%
Other 24 12%
Unknown 59 28%
Readers by discipline Count As %
Psychology 81 39%
Neuroscience 32 15%
Agricultural and Biological Sciences 4 2%
Social Sciences 4 2%
Engineering 4 2%
Other 18 9%
Unknown 65 31%