A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System

Authors

J.A. Camilleri, Heinrich-Heine-Universitat Dusseldorf
S.B. Eickhoff, Heinrich-Heine-Universitat Dusseldorf
S. Weis, Heinrich-Heine-Universitat Dusseldorf
J. Chen, Zhejiang University
J. Amunts, Heinrich-Heine-Universitat Dusseldorf
A. Sotiras, Washington University School of Medicine in St. Louis
S. Genon, Heinrich-Heine-Universitat Dusseldorf

Journal

Scientific Reports

Publication Date

2021

Volume

11

Issue

1

First Page

16896

Document Type

Open Access Publication

DOI

10.1038/s41598-021-96342-3

Rights and Permissions

Camilleri, J.A., Eickhoff, S.B., Weis, S. et al. A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System. Sci Rep 11, 16896 (2021). https://doi.org/10.1038/s41598-021-96342-3 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Recommended Citation

Camilleri, J.A.; Eickhoff, S.B.; Weis, S.; Chen, J.; Amunts, J.; Sotiras, A.; and Genon, S., "A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System." Scientific Reports. 11, 1. 16896 (2021).
https://digitalcommons.wustl.edu/open_access_pubs/10772