Cited by 21Cognitive Abilities and TestingFunctional Brain Connectivity StudiesNeural dynamics and brain function

Working Memory and Reasoning Benefit from Different Modes of Large-scale Brain Dynamics in Healthy Older Adults

Lebedev, Alexander V., Nilsson, Jonna, Lövdén, Martin·2018·Journal of Cognitive Neuroscience

Published version of a preprint

Abstract

Researchers have proposed that solving complex reasoning problems, a key indicator of fluid intelligence, involves the same cognitive processes as solving working memory tasks. This proposal is supported by an overlap of the functional brain activations associated with the two types of tasks and by high correlations between interindividual differences in performance. We replicated these findings in 53 older participants but also showed that solving reasoning and working memory problems benefits from different configurations of the functional connectome and that this dissimilarity increases with a higher difficulty load. Specifically, superior performance in a typical working memory paradigm ( n-back) was associated with upregulation of modularity (increased between-network segregation), whereas performance in the reasoning task was associated with effective downregulation of modularity. We also showed that working memory training promotes task-invariant increases in modularity. Because superior reasoning performance is associated with downregulation of modular dynamics, training may thus have fostered an inefficient way of solving the reasoning tasks. This could help explain why working memory training does little to promote complex reasoning performance. The study concludes that complex reasoning abilities cannot be reduced to working memory and suggests the need to reconsider the feasibility of using working memory training interventions to attempt to achieve effects that transfer to broader cognition.

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Crossref · 10.1162/jocn_a_01260

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  • Neuroimaging findings indicate that working memory performance correlates with greater network segregation, while fluid intelligence tasks show the reverse pattern.

    “Brain imaging data suggest that WM performance is associated with increased network segregation, whereas the opposite pattern occurs when participants are asked to solve fluid intelligence tasks (Lebedev, Nilsson, & Lövdén, 2018).”

    From Working memory training in typically developing children: A multilevel meta-analysis (2020) · cites this paper as Lebedev, Nilsson, & Lövdén, 2018

  • The meta-analysis findings are in line with evidence that working memory and fluid intelligence differ in their underlying neural mechanisms.

    “By contrast, our findings are in line with more recent evidence suggesting that WM and fluid intelligence differ from each other in terms of the underlying neural mechanisms (up-regulation and down-regulation of modularity; Lebedev, Nilsson, & Lövdén, 2018).”

    From Working memory training does not enhance older adults' cognitive skills: A comprehensive meta-analysis (2019) · cites this paper as Lebedev, Nilsson, & Lövdén, 2018

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References 76 · Cited by 21

Authors
Lebedev, A. V., Nilsson, J., & Lövdén, M.
Affiliations
Stockholm University (SE)
Venue
Journal of Cognitive Neuroscience
Publisher
MIT Press
Published
2018-07
Volume
30
Issue
7
Pages
1033-1046
ISSN
  • 0898-929X (ISSN-L)
  • 1530-8898
OpenAlex
W2791412468
Cited by
21
References
76
Citations by year
2025: 2 · 2024: 2 · 2023: 3 · 2022: 3 · 2021: 3 · 2020: 6 · 2019: 2
FWCI
3.60
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Cognitive Neuroscience (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • European Commission
  • Vetenskapsrådet
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
working memory, reasoning, fluid intelligence, functional connectome, network modularity, older adults, n-back task, working memory training, task difficulty, individual differences, brain dynamics
MeSH terms
Aged, Aging, Brain, Female, Humans, Image Processing, Computer-Assisted, Intelligence, Learning, Magnetic Resonance Imaging, Male, Memory, Short-Term, Models, Neurological, Neuropsychological Tests, Oxygen, Problem Solving, Nonlinear Dynamics

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