Temporal structure in sensorimotor variability: a stable trait, but what for?

Perquin M, Bompas A (2023)
Computational Brain and Behavior .

Zeitschriftenaufsatz | E-Veröff. vor dem Druck | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Perquin, MarlouUniBi; Bompas, Aline
Abstract / Bemerkung
Human performance shows substantial endogenous variability over time, and this variability is a robust marker of individual differences. Of growing interest to psychologists is the realisation that variability is not fully random, but often exhibits temporal dependencies. However, their measurement and interpretation come with several controversies. Furthermore, their potential benefit for studying individual differences in healthy and clinical populations remains unclear. Here, we gather new and archival datasets featuring 11 sensorimotor and cognitive tasks across 526 participants, to examine individual differences in temporal structures. We first investigate intra-individual repeatability of the most common measures of temporal structures — to test their potential for capturing stable individual differences. Secondly, we examine inter-individual differences in these measures using: (1) task performance assessed from the same data, (2) meta-cognitive ratings of on-taskness from thought probes occasionally presented throughout the task, and (3) self-assessed attention-deficit related traits. Across all datasets, autocorrelation at lag 1 and Power Spectra Density slope showed high intra-individual repeatability across sessions and correlated with task performance. The Detrended Fluctuation Analysis slope showed the same pattern, but less reliably. The long-term component (d) of the ARFIMA(1,d,1) model showed poor repeatability and no correlation to performance. Overall, these measures failed to show external validity when correlated with either mean subjective attentional state or self-assessed traits between participants. Thus, some measures of serial dependencies may be stable individual traits, but their usefulness in capturing individual differences in other constructs typically associated with variability in performance seems limited. We conclude with comprehensive recommendations for researchers.
Erscheinungsjahr
2023
Zeitschriftentitel
Computational Brain and Behavior
eISSN
2522-087X
Page URI
https://pub.uni-bielefeld.de/record/2943823

Zitieren

Perquin M, Bompas A. Temporal structure in sensorimotor variability: a stable trait, but what for? Computational Brain and Behavior . 2023.
Perquin, M., & Bompas, A. (2023). Temporal structure in sensorimotor variability: a stable trait, but what for? Computational Brain and Behavior . https://doi.org/10.1007/s42113-022-00162-1
Perquin, Marlou, and Bompas, Aline. 2023. “Temporal structure in sensorimotor variability: a stable trait, but what for?”. Computational Brain and Behavior .
Perquin, M., and Bompas, A. (2023). Temporal structure in sensorimotor variability: a stable trait, but what for? Computational Brain and Behavior .
Perquin, M., & Bompas, A., 2023. Temporal structure in sensorimotor variability: a stable trait, but what for? Computational Brain and Behavior .
M. Perquin and A. Bompas, “Temporal structure in sensorimotor variability: a stable trait, but what for?”, Computational Brain and Behavior , 2023.
Perquin, M., Bompas, A.: Temporal structure in sensorimotor variability: a stable trait, but what for? Computational Brain and Behavior . (2023).
Perquin, Marlou, and Bompas, Aline. “Temporal structure in sensorimotor variability: a stable trait, but what for?”. Computational Brain and Behavior (2023).

Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

References

Daten bereitgestellt von Europe PubMed Central.

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Quellen

PMID: 36618326
PubMed | Europe PMC

Preprint: 10.1101/817916

Suchen in

Google Scholar