Decoding episodic autobiographical memory in naturalistic virtual reality.
Emotion
Episodic autobiographical memory
Episodic memory
Prediction
Self
Virtual reality
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
27 10 2024
27 10 2024
Historique:
received:
25
03
2024
accepted:
17
10
2024
medline:
28
10
2024
pubmed:
28
10
2024
entrez:
28
10
2024
Statut:
epublish
Résumé
Episodic autobiographical memory (EAM) is a long-term memory system of personally experienced events with their context - what, where, when - and subjective elements, e.g., emotions, thoughts, or self-reference. EAM formation has rarely been studied in a controlled, real-life-like paradigm, and there is no predictive model of long-term retrieval from self-rated subjective experience at encoding. The present longitudinal study, with three surprise free recall memory tests immediately, one-week and one-month after encoding, investigated incidental encoding of EAM in an immersive virtual environment where 30 participants either interacted with or observed specific events of varying emotional valences with simultaneous physiological recordings. The predictive analyses highlight the temporal dynamics of the predictors of EAM from subjective ratings at encoding: common characteristics related to sense of remembering and infrequency of real-life encounter of the event were identified over time, but different variables become relevant at different time points, such as the emotion and mental imagery or prospective aspects. This dynamic and time-dependent role of memory predictors challenges traditional views of a uniform influence of encoding factors over time. Current evidence for the multiphasic nature of memory formation points to the role of different mechanisms at play during encoding but also consolidation and subsequent retrieval.
Identifiants
pubmed: 39463396
doi: 10.1038/s41598-024-76944-3
pii: 10.1038/s41598-024-76944-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25639Subventions
Organisme : Fondation de l'École Polytechnique
ID : AMX
Organisme : Agence Nationale de la Recherche
ID : ANR-17-CE36-0009-01
Informations de copyright
© 2024. The Author(s).
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