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Unicorn, Hare, or Tortoise? Using Machine Learning to Predict Working Memory Training Performance Cover

Unicorn, Hare, or Tortoise? Using Machine Learning to Predict Working Memory Training Performance

Open Access
|Sep 2023

Authors

Yi Feng

yif12@uci.edu

University of California, Irvine, School of Education, School of Social Sciences (Department of Cognitive Sciences), Irvine, California

Anja Pahor

apahor@uci.edu

University of California, Riverside, Department of Psychology, Riverside, California; Northeastern University, Department of Psychology, Boston, Massachusetts, US; University of Maribor, Department of Psychology, Maribor

Aaron R. Seitz

aseitz@ucr.edu

University of California, Riverside, Department of Psychology, Riverside, California; Northeastern University, Department of Psychology, Boston, Massachusetts

Dennis L. Barbour

dbarbour@wustl.edu

Washington University in St. Louis, Department of Biomedical Engineering, St. Louis, Missouri

Susanne M. Jaeggi

smjaeggi@uci.edu

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

© 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.