Working Memory Models and Assessment Methods: A Review

This paper provides a comprehensive overview of working memory, a crucial cognitive construct for learning, reasoning, and intellectual abilities. It introduces Baddeley and Hitch's multi-component model, highlighting the construct's essential role in facilitating learning, comprehension,...

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Bibliographic Details
Published in:2024 20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024 - Conference Proceedings
Main Author: Abidin N.A.Z.; Yassin A.I.M.; Ali M.S.A.M.; Mansor W.; Azhan M.N.M.; Rozlan M.F.R.M.; Jahidin A.H.; Mahmoodin Z.; Eskandari F.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193943115&doi=10.1109%2fCSPA60979.2024.10525366&partnerID=40&md5=2e3e08b4461af7630c6489cde8c3ae03
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Summary:This paper provides a comprehensive overview of working memory, a crucial cognitive construct for learning, reasoning, and intellectual abilities. It introduces Baddeley and Hitch's multi-component model, highlighting the construct's essential role in facilitating learning, comprehension, problem-solving, and attention. The paper then analyzes four key models: Baddeley's multicomponent framework, Cowan's integrated memory network model, Engle's attentional control-based model, and Oberauer's tripartite structure, exploring their shared and differing perspectives. Recent studies on working memory using electroencephalogram are reviewed, identifying core frequency bands associated with cognitive states and predicting individual working memory capacity from electroencephalogram. Conventional working memory assessment methods are also discussed, emphasizing their relative advantages and limitations based on specific goals and practical considerations. Overall, this paper integrates theoretical models, neural correlates, and practical applications to provide a comprehensive overview of working memory research, highlighting its interdisciplinary nature for future studies. © 2024 IEEE.
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DOI:10.1109/CSPA60979.2024.10525366