title: Adaptive experiments with a multivariate Elo-type algorithm.
abstract: The present article introduces the multivariate Elo-type algorithm (META), which is inspired by the Elo rating system, a tool for the measurement of the performance of chess players. The META is intended for adaptive experiments with correlated traits. The relationship of the META to other existing procedures is explained, and useful variants and modifications are discussed. The META was investigated within three simulation studies. The gain in efficiency of the univariate Elo-type algorithm was compared to standard univariate procedures; the impact of using correlational information in the META was quantified; and the adaptability to learning and fatigue was investigated. Our results show that the META is a powerful tool to efficiently control task performance in a short time period and to assess correlated traits. The R code of the simulations, the implementation of the META in MATLAB, and an example of how to use the META in the context of neuroscience are provided in supplemental materials.
authors.affiliation: Department of Psychology and Sport Sciences, Westfälische Wilhelms-Universität, Fliednerstr. 21, 48149, Münster, Germany, doebler@uni-muenster.de.
authors.forename: Philipp
authors.initials: P
authors.lastname: Doebler
authors.forename: Mohsen
authors.initials: M
authors.lastname: Alavash
authors.forename: Carsten
authors.initials: C
authors.lastname: Giessing
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D011597:Psychomotor Performance; D012107:Research Design; D013647:Task Performance and Analysis; D013997:Time Factors