Unicorn, Hare, or Tortoise? Using Machine Learning to Predict Working Memory Training Performance
Authors
University of California, Irvine, School of Education, School of Social Sciences (Department of Cognitive Sciences), Irvine, California
University of California, Riverside, Department of Psychology, Riverside, California; Northeastern University, Department of Psychology, Boston, Massachusetts, US; University of Maribor, Department of Psychology, Maribor
University of California, Riverside, Department of Psychology, Riverside, California; Northeastern University, Department of Psychology, Boston, Massachusetts
Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri
University of California, Irvine, School of Education, School of Social Sciences (Department of Cognitive Sciences), Irvine, California; Northeastern University, Department of Psychology, Boston, Massachusetts
DOI: https://doi.org/10.5334/joc.319 | Journal eISSN: 2514-4820
Language: English
Submitted on: Feb 16, 2023
Accepted on: Aug 15, 2023
Published on: Sep 4, 2023
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
© 2023 Yi Feng, Anja Pahor, Aaron R. Seitz, Dennis L. Barbour, Susanne M. Jaeggi, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.