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Distributed Patterns of Reactivation Predict Vividness of Recollection
- Source :
- Journal of cognitive neuroscience. 27(10)
- Publication Year :
- 2015
-
Abstract
- According to the principle of reactivation, memory retrieval evokes patterns of brain activity that resemble those instantiated when an event was first experienced. Intuitively, one would expect neural reactivation to contribute to recollection (i.e., the vivid impression of reliving past events), but evidence of a direct relationship between the subjective quality of recollection and multiregional reactivation of item-specific neural patterns is lacking. The current study assessed this relationship using fMRI to measure brain activity as participants viewed and mentally replayed a set of short videos. We used multivoxel pattern analysis to train a classifier to identify individual videos based on brain activity evoked during perception and tested how accurately the classifier could distinguish among videos during mental replay. Classification accuracy correlated positively with memory vividness, indicating that the specificity of multivariate brain patterns observed during memory retrieval was related to the subjective quality of a memory. In addition, we identified a set of brain regions whose univariate activity during retrieval predicted both memory vividness and the strength of the classifier's prediction irrespective of the particular video that was retrieved. Our results establish distributed patterns of neural reactivation as a valid and objective marker of the quality of recollection.
- Subjects :
- Adult
Male
Brain Mapping
Recall
Brain activity and meditation
Cognitive Neuroscience
media_common.quotation_subject
Memory, Episodic
Univariate
Brain
Magnetic Resonance Imaging
Young Adult
Perception
Classifier (linguistics)
Mental Recall
Visual Perception
Humans
Female
Set (psychology)
Subjective quality
Psychology
Social psychology
Cognitive psychology
Event (probability theory)
media_common
Subjects
Details
- ISSN :
- 15308898
- Volume :
- 27
- Issue :
- 10
- Database :
- OpenAIRE
- Journal :
- Journal of cognitive neuroscience
- Accession number :
- edsair.doi.dedup.....82cb90d6a7b1871033562449757233ec