ArticleDevelopmental science2020
Mapping differential responses to cognitive training using machine learning.
Article in Developmental science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Training-dependent transfer within a set of nested tasks.Quarterly journal of experimental psychology (2006) · 2021Trial
- The topology of adolescent mental health.medRxiv : the preprint server for health sciences · 2026Article
- Up, down, and all around? Deciphering the boundary conditions for training-induced transfer effects within a set of hierarchically nested tasks.Quarterly journal of experimental psychology (2006) · 2026Article
- Machine Learning Approach for Predicting Older Adults' Responsiveness to Cognitive Training Interventions: Data from the ACTIVE Study.Journal of Intelligence · 2026Article
- Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy.Brain sciences · 2025Review
- Article
- Statistical power for cluster analysis.BMC bioinformatics · 2022Article
- A Machine Learning Approach to Personalize Computerized Cognitive Training Interventions.Frontiers in artificial intelligence · 2022Article
- Two Pathways to Self-Harm in Adolescence.Journal of the American Academy of Child and Adolescent Psychiatry · 2021Article
- The promise of machine learning in predicting treatment outcomes in psychiatry.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2021Article
- Mapping differential responses to cognitive training using machine learning.Developmental science · 2020Article
- Transdiagnostic Brain Mapping in Developmental Disorders.Current biology : CB · 2020Article
- MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain.Frontiers in neuroinformatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self-organizing maps (SOMs)-a type of simple artificial neural network-to represent multivariate cognitive training data, and then tested whether the way tasks are represented changed as a result of training. The patterns of change we observed in the SOM weight matrices implied that the processes drawn upon to perform WM tasks changed following training. This was then combined with K-means clustering to identify distinct groups of children who respond to the training in different ways. Firstly, the K-means clustering was applied to an independent large sample (N = 616, M
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.