Working memory training mostly engages general-purpose large-scale networks for learning
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HighlightsWe performed the first meta‐analysis of fMRI studies on working memory (WM) training.We demonstrated that WM training modulates only the canonical WM network.This general‐purpose brain network was also modulated by perceptual‐motor learning.Only DLPFC/VLPFC was highlighted in WM training studies. ABSTRACT The present meta‐analytic study examined brain activation changes following working memory (WM) training, a form of cognitive training that has attracted considerable interest. Comparisons with perceptual‐motor (PM) learning revealed that WM training engages domain‐general large‐scale networks for learning encompassing the dorsal attention and salience networks, sensory areas, and striatum. Also the dynamics of the training‐induced brain activation changes within these networks showed a high overlap between WM and PM training. The distinguishing feature for WM training was the consistent modulation of the dorso‐ and ventrolateral prefrontal cortex (DLPFC/VLPFC) activity. The strongest candidate for mediating transfer to similar untrained WM tasks was the frontostriatal system, showing higher striatal and VLPFC activations, and lower DLPFC activations after training. Modulation of transfer‐related areas occurred mostly with longer training periods. Overall, our findings place WM training effects into a general perception‐action cycle, where some modulations may depend on the specific cognitive demands of a training task.
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Neuroimaging evidence indicates that the frontostriatal system mediates near transfer, while dorsolateral and ventrolateral prefrontal regions use domain-general networks that support broader learning.
“neuroimaging research suggests that the frontostriatal system mediates near transfer effects whereas the dorso- and ventrolateral prefrontal cortices appear to employ domain-general networks that facilitate learning more wide5.”
From Near transfer to an unrelated N-back task mediates the effect of N-back working memory training on matrix reasoning (2022) · cites this paper as [5]
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Cognitive and Motor Perseveration Are Associated in Older Adults
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Deciphering Mechanisms of Anxiety Influences on Hazard Identification in Construction Situations
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Age-Independent Transfer After Successful Associative Mnemonic Training
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- Authors
- Salmi, J., Nyberg, L., & Laine, M.
- Affiliations
- Åbo Akademi University (FI)
- Umeå University (SE)
- Corresponding author
- Salmi, Juha
- Venue
- Neuroscience & Biobehavioral Reviews
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 93
- Pages
- 108-122
- ISSN
- 0149-7634 (ISSN-L)
- OpenAlex
- W2793100538
- Semantic Scholar
- 90188f1670aaedf220bed69fb42b844df6e5619c
- Cited by
- 70
- References
- 133
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 7 · 2023: 9 · 2022: 13 · 2021: 13 · 2020: 15 · 2019: 6 · 2018: 1
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Neuroscience & Biobehavioral Reviews (publishedVersion)
- PubMed (publishedVersion)
- Työväentutkimus Vuosikirja (submittedVersion)
- Åbo Akademi University Research Portal (submittedVersion)
- Funders
- Åbo Akademi University Endowment
- Academy of Finland (#260276)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Functions and Memory
- Keywords
- working memory training, cognitive training, perceptual-motor learning, dorsal attention network, salience network, striatum, dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, frontostriatal circuits, transfer of training, training duration, perception-action cycle
- MeSH terms
- Animals, Brain, Humans, Magnetic Resonance Imaging, Memory, Short-Term, Neural Pathways, Neuronal Plasticity, Practice, Psychological, Executive Function
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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