Unicorn, hare, or tortoise? Using machine learning to predict working memory training performance

Authors

Yi Feng, University of California
Anja Pahor, University of California
Aaron R Seitz, University of California
Dennis L Barbour, Washington University School of Medicine in St. Louis
Susanne M Jaeggi, University of California

Journal

Journal of Cognition

Publication Date

1-1-2023

Volume

6

Issue

1

First Page

53

Document Type

Open Access Publication

DOI

10.5334/joc.319

Rights and Permissions

Feng Y, Pahor A, Seitz AR, Barbour DL, Jaeggi SM. Unicorn, Hare, or Tortoise? Using Machine Learning to Predict Working Memory Training Performance. J Cogn. 2023 Sep 4;6(1):53. doi: 10.5334/joc.319. © 2023 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http:// creativecommons.org/ licenses/by/4.0/. Journal of Cognition is a peerreviewed open access journal published by Ubiquity Press.

Recommended Citation

Feng, Yi; Pahor, Anja; Seitz, Aaron R; Barbour, Dennis L; and Jaeggi, Susanne M, "Unicorn, hare, or tortoise? Using machine learning to predict working memory training performance." Journal of Cognition. 6, 1. 53 (2023).
https://digitalcommons.wustl.edu/oa_4/2372

Department

ICTS (Institute of Clinical and Translational Sciences)

Additional Links

Supplemental material is available for this article at publisher site.