How the Complexity of Psychological Processes Reframes the Issue of Reproducibility in Psychological Science

Christophe Gernigon*, Ruud den Hartigh, Robin R. Vallacher, Paul Van Geert

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

11 Citations (Scopus)
101 Downloads (Pure)

Abstract

In the past decade, various recommendations have been published to enhance the methodological rigor and publication standards in psychological science. However, adhering to these recommendations may have limited impact on the reproducibility of causal effects as long as psychological phenomena continue to be viewed as decomposable into separate and additive statistical structures of causal relationships. In this article, we show that (a) psychological phenomena are patterns emerging from nondecomposable and nonisolable complex processes that obey idiosyncratic nonlinear dynamics, (b) these processual features jeopardize the chances of standard reproducibility of statistical results, and (c) these features call on researchers to reconsider what can and should be reproduced, that is, the psychological processes per se, and the signatures of their complexity and dynamics. Accordingly, we argue for a greater consideration of process causality of psychological phenomena reflected by key properties of complex dynamical systems (CDSs). This implies developing and testing formal models of psychological dynamics, which can be implemented by computer simulation. The scope of the CDS paradigm and its convergences with other paradigms are discussed regarding the reproducibility issue. Ironically, the CDS approach could account for both reproducibility and nonreproducibility of the statistical effects usually sought in mainstream psychological science.
Original languageEnglish
Pages (from-to)952-977
Number of pages26
JournalPerspectives on Psychological Science
Volume19
Issue number6
Early online date14-Aug-2023
DOIs
Publication statusPublished - Nov-2024

Keywords

  • causality
  • complexity
  • reproducibility
  • complex systems
  • dynamical systems
  • intrinsic dynamics
  • ergodicity
  • nonlinearity
  • formal models
  • computer simulation

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