Comparing the effects of different methodological decisions on the error-related negativity and its association with behaviour and gender

Sandre, Aislinn; Banica, Iulia; Riesel, Anja; Klawohn, Julia; Weinberg, Anna

doi:10.1016/j.ijpsycho.2020.06.016

Cited by 58 publications

(58 citation statements)

References 178 publications

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“…In many areas of neuroscience research, including ERP studies, a common approach for calculating difference scores is to subtract activity during one condition ( X , the subtrahend) from activity during another condition ( Y , the minuend; Y − X ). Another approach increasing in popularity is a residualized difference score (e.g., Crane et al., 2021; Hanna et al., 2020; Klawohn et al., 2020; Macatee et al., 2020; Meyer et al., 2017; Muir et al., 2020; Sandre et al., 2020; Schroder et al., 2020; Suor et al, in press). A residualized difference score is the difference between the actual score ( Y ) and predicted score (

\overset{Y}{true}

) using a linear regression equation (

Y ‐ \hat{Y}

), wherein Y scores are regressed on X .…”

Section: Introductionmentioning

confidence: 99%

Evaluating the internal consistency of subtraction‐based and residualized difference scores: Considerations for psychometric reliability analyses of event‐related potentials

2021

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In studies of event‐related brain potentials (ERPs), difference scores between conditions in a task are frequently used to isolate neural activity for use as a dependent or independent variable. Adequate score reliability is a prerequisite for studies examining relationships between ERPs and external correlates, but there is no extensive treatment on the suitability of the various available approaches to estimating difference score reliability that focus on ERP research. In the present study, we provide formulas from classical test theory and generalizability theory for estimating the internal consistency of subtraction‐based and residualized difference scores. These formulas are then applied to error‐related negativity (ERN) and reward positivity (RewP) difference scores from the same sample of 117 participants. Analyses demonstrate that ERN difference scores can be reliable, which supports their use in studies of individual differences. However, RewP difference scores yielded poor reliability due to the high correlation between the constituent reward and non‐reward ERPs. Findings emphasize that difference score reliability largely depends on the internal consistency of constituent scores and the correlation between those scores. Furthermore, generalizability theory yields more suitable estimates of internal consistency for subtraction‐based difference scores than classical test theory. We conclude that ERP difference scores can show adequate reliability and be useful for isolating neural activity in studies of individual differences.

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\overset{Y}{true}

) using a linear regression equation (

Y ‐ \hat{Y}

), wherein Y scores are regressed on X .…”

Section: Introductionmentioning

confidence: 99%

Evaluating the internal consistency of subtraction‐based and residualized difference scores: Considerations for psychometric reliability analyses of event‐related potentials

2021

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“…This ERP component of negative polarity peaks ~80-150 milliseconds after an erroneous motor response in speeded tasks, is largest at midline frontal and central electrode sites, and originates from the anterior cingulate cortex (Falkenstein et al, 1991;Gehring et al, 1993) . A recent paper (Sandre et al, 2020) highlighted cross-study variability in the selection of reference location, baseline correction, and electrode site from which signal amplitudes were measured. The authors systematically compared 72 commonly used preprocessing pipelines to examine their effects on the resulting ERN amplitude.…”

Section: Researchmentioning

confidence: 99%

Making ERP Research More Transparent: Guidelines for Preregistration

Paul¹,

Govaart²,

Schettino³

2020

Preprint

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Confirmation bias and pressure to publish may prompt the (unconscious) exploration of various methodological options and reporting only the ones that lead to a (statistically) significant outcome. This undisclosed analytic flexibility increases false positive results and inflates effect sizes, ultimately creating a skewed representation of knowledge. This issue is particularly relevant in EEG research, where a myriad of preprocessing and analysis pipelines can be used to extract information from complex multidimensional data. One solution to limit undisclosed analytic flexibility is preregistration: researchers write a time-stamped, publicly accessible research plan with hypotheses, data collection plan, and intended preprocessing and statistical analyses before the start of a research project. In this manuscript, we present an overview of the problems associated with undisclosed analytic flexibility (particularly in human neuroimaging and electrophysiology), discuss why and how EEG researchers would benefit from adopting preregistration, provide guidelines and examples on how to preregister data preprocessing and analysis steps in typical ERP studies, and conclude by addressing common rebuttals as well as clarifying possibilities and limitations of this open science practice.

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“…Registered reports, which detail information regarding stimulus presentation and data analysis are therefore particularly helpful in the context of multi-laboratory studies. Although few such studies exists for psychophysiological data (Miltner et al, 1994;Drisdelle et al, 2017;Sandre et al, 2020) the variability in preprocessing and data analysis pipelines is comparable to the flexibility and diversity of methods observed in neuroimaging. This emphasizes the need for a detailed description of the methods used in publications (Keil et al, 2014).…”

Section: Data and Analysis Pipeline Sharingmentioning

confidence: 99%

Open Science in Psychophysiology: An Overview of Challenges and Emerging Solutions

Garrett-Ruffin¹,

Hindash²,

Kaczkurkin³

et al. 2020

Preprint

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The present review is the result of a one-day workshop on open science, held at the Annual Meeting of the Society for Psychophysiological Research in Washington, DC, September 2019. The contributors represent psychophysiological researchers at different career stages and from a wide spectrum of institutions. We discuss the state of open science in psychophysiology as perceived from different perspectives, define key challenges, and propose a set of measures that are intended to facilitate open science practices.

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Comparing the effects of different methodological decisions on the error-related negativity and its association with behaviour and gender

Cited by 58 publications

References 178 publications

Evaluating the internal consistency of subtraction‐based and residualized difference scores: Considerations for psychometric reliability analyses of event‐related potentials

Evaluating the internal consistency of subtraction‐based and residualized difference scores: Considerations for psychometric reliability analyses of event‐related potentials

Making ERP Research More Transparent: Guidelines for Preregistration

Open Science in Psychophysiology: An Overview of Challenges and Emerging Solutions

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