December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
The impact of analytic choices on detectability of behavioral oscillations in dense sampling studies
Author Affiliations & Notes
  • René Michel
    Institute of Psychology, University of Münster, Münster, Germany
    Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
  • Elio Balestrieri
    Institute of Psychology, University of Münster, Münster, Germany
    Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
  • Samuel Recht
    Department of Experimental Psychology, University of Oxford, Oxford, UK
  • Laura Dugué
    Université de Paris, INCC UMR 8002, CNRS, F-75006 Paris, France
    Institut Universitaire de France (IUF), Paris, France
  • Niko A. Busch
    Institute of Psychology, University of Münster, Münster, Germany
    Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
  • Footnotes
    Acknowledgements  This work was supported by an ANR-DFG grant to Niko Busch (BU 2400/8-1) and Laura Dugué (J18P08ANR00).
Journal of Vision December 2022, Vol.22, 3891. doi:https://doi.org/10.1167/jov.22.14.3891
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      René Michel, Elio Balestrieri, Samuel Recht, Laura Dugué, Niko A. Busch; The impact of analytic choices on detectability of behavioral oscillations in dense sampling studies. Journal of Vision 2022;22(14):3891. https://doi.org/10.1167/jov.22.14.3891.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

In the last decade, the dense sampling technique, a psychophysical approach to test rhythmicities in perceptual and cognitive functions, has gained in popularity. Typically, a brief event such as a visual cue is presented at the beginning of each trial to reset the rhythm associated with the cognitive function of interest, thereby aligning it across trials. This enables the experimenter to sample behavioral performance at different phases of this rhythm by presenting target stimuli at various intervals after the reset event. Thus, the time course of behavioral performance is expected to reflect the rhythmicity of the tested perceptual function. However, testing behavioral data for rhythmicities requires numerous choices about various signal processing and spectral analysis parameters, amounting to a virtually infinite number of potential plausible analysis pipelines. Unfortunately, while the field of dense sampling shows great variability of analytic decisions, the impact of these choices on results is unknown. Our study systematically compares common analysis pipelines found in the literature. We created surrogate data mimicking prototypical dense sampling studies by simulating single trial data with or without an underlying rhythm of varying amplitude. We then analyzed these data using various analysis pipelines (combinations of preprocessing, frequency decomposition and multiple comparison correction methods) and tested their performance at detecting the simulated rhythm. Critically, we found that pipelines differed in their signal detection performance as measured by the sensitivity index d’. For example, across all types of simulated trends, pipelines including second-order detrending performed better than those including first-order detrending or simple demeaning. Based on these findings, we provide best practice recommendations for future dense sampling studies.

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