TY - JOUR

T1 - A Multivariate Method for Dynamic System Analysis

T2 - Multivariate Detrended Fluctuation Analysis Using Generalized Variance

AU - Wallot, Sebastian

AU - Irmer, Julien Patrick

AU - Tschense, Monika

AU - Kuznetsov, Nikita

AU - Højlund, Andreas

AU - Dietz, Martin

N1 - Funding Information: SW acknowledges funding from the German Science Foundation (DFG; Heisenberg programme, funding ID: 442405852). The project acknowledges funding from the Danish National Research Foundation's grant to CFIN and the MINDLab grant from the Danish Ministry of Science, Technology and Innovation. Publisher Copyright: © 2023 The Authors. Topics in Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society.

PY - 2023/9/14

Y1 - 2023/9/14

N2 - Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation between these time series when estimating fractal properties. First, we briefly describe how fractal fluctuations have advanced a dynamic system understanding of cognition. Then, we describe mvDFA in detail and highlight some of the advantages of the approach for simulated data. Furthermore, we show how mvDFA can be used to investigate empirical multivariate data using electroencephalographic recordings during a time-estimation task. We discuss this methodological development within the framework of interaction-dominant dynamics. Moreover, we outline how the availability of multivariate analyses can inform theoretical developments in the area of dynamic systems in human behavior.

AB - Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation between these time series when estimating fractal properties. First, we briefly describe how fractal fluctuations have advanced a dynamic system understanding of cognition. Then, we describe mvDFA in detail and highlight some of the advantages of the approach for simulated data. Furthermore, we show how mvDFA can be used to investigate empirical multivariate data using electroencephalographic recordings during a time-estimation task. We discuss this methodological development within the framework of interaction-dominant dynamics. Moreover, we outline how the availability of multivariate analyses can inform theoretical developments in the area of dynamic systems in human behavior.

KW - Detrended fluctuation analysis

KW - Dynamic systems

KW - Interaction-dominant dynamics

KW - Multivariate analysis

KW - R package

KW - Time estimation

KW - Psychology

UR - http://www.scopus.com/inward/record.url?scp=85170689774&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/0fcb8926-34ff-3ce6-a10f-e20bedaaf31f/

U2 - 10.1111/tops.12688

DO - 10.1111/tops.12688

M3 - Journal articles

C2 - 37706618

AN - SCOPUS:85170689774

JO - Topics in Cognitive Science

JF - Topics in Cognitive Science

SN - 1756-8757

ER -