Search PubMedSearch

PubMed · 42711482

The asymmetry of working memory training transfer: A systematic review and meta-analysis.

Abstract

Working memory (WM) training is widely used to enhance cognitive performance; however, its transfer to untrained tasks remains controversial. Traditional theories emphasize task similarity as the primary determinant of training transfer, but they cannot fully explain emerging evidence of asymmetric transfer across tasks. Two directional transfer hypotheses are proposed here to explain this asymmetry: the resource-based transfer advantage hypothesis predicts stronger transfer from more to less cognitively demanding tasks, whereas the ability-based transfer advantage hypothesis predicts stronger transfer from tasks engaging broader task-general abilities to tasks engaging task-specific narrower abilities. The contrast between span and updating paradigms provides an informative framework for distinguishing these accounts, because updating tasks are generally more cognitively demanding, whereas span tasks involve broader abilities. Accordingly, we conducted a three-level meta-analysis of 55 studies (208 effect sizes; N = 3,492). The results showed that updating training transferred reliably to span tasks (g = 0.176, p < .001), whereas span training did not reliably transfer to updating tasks (g = 0.048, p = .453), supporting the resource-based account. This advantage of updating training also extended to non-WM outcomes and was more pronounced at lower training doses, in non-adult samples, and with verbal stimuli. Together, these findings extend WM transfer theory beyond task similarity by highlighting the importance of cognitive demand and offer guidance for WM training design.

Explore related subjects

Keep this discovery

BibTeXRIS

Jiayi Fu, Yuxin Zhou, Mingliang Yuan, Ying Cai. 2026-09-08. The asymmetry of working memory training transfer: A systematic review and meta-analysis.. https://doi.org/10.3758/s13423-026-02994-5

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related citations

Response-optimised training improves learning of a complex motor task and closely related motor tasks.

Regular physical exercise is essential for promoting healthy aging and longevity. In older adults with varying physical and cognitive decline, optimising exercise interventions is crucial to maximise benefits. A promising approach to achieve this goal is by adjusting task demands to individual abilities in turn preventing over- or underloading their abilities. In the field of motor learning, it is currently unclear whether such an optimised training improves not only performance on the trained task but also transfers to untrained motor and cognitive tasks. We conducted a randomized, single-blinded, 6-week dynamic balance training (DBT) with healthy older adults (n&#xa0;=&#xa0;30). Training was tailored to individual balance ability. Participants were assigned to either suboptimal (high or low difficulty) or optimal (moderate difficulty) training groups. Transfer effects were assessed via cognitive tasks (memory and executive) and motor tasks (untrained DBT variations and other balance tasks) measured pre-, mid- and post-intervention. Multivariate longitudinal statistical analysis showed higher performance gains in the optimal training group in three out of six sessions compared to the suboptimal groups, especially under testing conditions with high task demands. The optimal group also showed greater improvements in near motor transfer tasks mid- and post-intervention, while no significant differences were observed in the cognitive tasks. Within-group DBT learning positively correlated with transfer gains, highlighting the role of training response in achieving transfer. In conclusion, optimised task difficulty in balance training enhances both task-specific performance and related motor skills, supporting the use of personalised interventions to maintain function and independence in older adults.

Humans

Dissociable neural mechanisms of cognitive enhancement through transcranial stimulation and behavioral training.

BACKGROUND: Transcranial direct current stimulation (tDCS) and adaptive working memory (WM) training are promising cognitive enhancement approaches; however, their neural mechanisms and potential synergies remain poorly understood. OBJECTIVE: We directly compared how tDCS and WM training modulate neural oscillations during WM performance and examined whether combining both interventions produces additive effects. METHODS: We randomized 112 healthy adults into four groups: control (sham tDCS&#xa0;+&#xa0;non-adaptive 1-back), tDCS-only (active tDCS&#xa0;+&#xa0;non-adaptive 1-back), training-only (sham tDCS&#xa0;+&#xa0;adaptive n-back training), or combined (active tDCS&#xa0;+&#xa0;adaptive training). Participants underwent five daily intervention sessions. We recorded high-density EEG during transfer n-back tasks at baseline, post-intervention, and one-week follow-up. RESULTS: All active interventions improved WM performance relative to the control group, with the combined group showing the largest gains (n-back accuracy: +15.6% vs.&#xa0;+&#xa0;10.1% tDCS-only, +9.7% training-only, +0.7% control; all p&#xa0;<&#xa0;0.001). Critically, tDCS selectively increased gamma-band (30-50&#xa0;Hz) power in the frontal and parietal regions (cluster p&#xa0;=&#xa0;0.018, d&#xa0;>&#xa0;1.0), whereas WM training enhanced frontal theta-band (4-8&#xa0;Hz) power and theta-gamma phase-amplitude coupling (both cluster p&#xa0;<&#xa0;0.012, d&#xa0;>&#xa0;0.85). The combined group exhibited both neural signatures. Brain-behavior correlations revealed dissociable relationships: gamma increases predicted n-back accuracy improvements (r&#xa0;=&#xa0;0.61, p&#xa0;<&#xa0;0.001), whereas theta enhancements correlated with operation span gains (r&#xa0;=&#xa0;0.58, p&#xa0;=&#xa0;0.002). CONCLUSIONS: tDCS and WM training enhance cognition through distinct yet complementary neural mechanisms: tDCS via gamma-mediated cortical excitability and WM training via theta-mediated cognitive control. These findings provide neurophysiological evidence for multimodal enhancement strategies that target parallel pathways within WM networks.

Humans

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89-0.94) in cross-validation and 0.88 (95% CI: 0.85-0.91) in independent validation, outperforming clinical models (&#x394;AUC = +0.18, p < 0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression-free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.

Humans