Contemporary neurocognitive models of memory: A descriptive comparative analysis.

Evolution Homeostasis Memory systems Neural networks Predictive coding

Journal

Neuropsychologia
ISSN: 1873-3514
Titre abrégé: Neuropsychologia
Pays: England
ID NLM: 0020713

Informations de publication

Date de publication:
29 Feb 2024
Historique:
received: 03 11 2023
revised: 27 02 2024
accepted: 27 02 2024
medline: 3 3 2024
pubmed: 3 3 2024
entrez: 2 3 2024
Statut: aheadofprint

Résumé

The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.

Identifiants

pubmed: 38430963
pii: S0028-3932(24)00061-7
doi: 10.1016/j.neuropsychologia.2024.108846
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

108846

Informations de copyright

Copyright © 2024. Published by Elsevier Ltd.

Déclaration de conflit d'intérêts

Declaration of competing interest The author declare that she has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Alba Marcela Zárate-Rochín (AM)

Instituto de Investigaciones Cerebrales, Universidad Veracruzana, Dr. Castelazo Ayala s/n, Industrial Animas, 91190, Xalapa-Enríquez, Veracruz, Mexico. Electronic address: mzarateroc@gmail.com.

Classifications MeSH