
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/">
  <dc:title xml:lang="eng">Garden of forking paths in ERP research – Effects of varying pre-processing and analysis steps in an N400 experiment</dc:title>
  <dc:source>Psychophysiology</dc:source>
  <dc:source>str. 1-18</dc:source>
  <dc:date>2024</dc:date>
  <dc:subject xml:lang="eng">KEYWORDS: event-related potentials, garden of forking paths, methodology, multiverse analysis, statistics</dc:subject>
  <dc:language>eng</dc:language>
  <dc:publisher>	Wiley-Blackwell on behalf of the Society for Psychophysiological Research</dc:publisher>
  <dc:creator id="https://orcid.org/0000-0003-0216-989X">Šoškić, Anđela</dc:creator>
  <dc:creator id="https://orcid.org/0000-0003-3517-9680">Styles, Suzy J.</dc:creator>
  <dc:creator id="https://orcid.org/0000-0002-2789-015X">Kappenman, Emily S.</dc:creator>
  <dc:creator id="https://orcid.org/0000-0002-3954-1125">Ković, Vanja</dc:creator>
  <dc:rights>All rights reserved</dc:rights>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:identifier>https://phaidrabg.bg.ac.rs/o:34248</dc:identifier>
  <dc:identifier>doi:10.1111/psyp.14628</dc:identifier>
  <dc:identifier>ISSN: 0048-5772</dc:identifier>
  <dc:format>application/pdf</dc:format>
  <dc:format>1461602 bytes</dc:format>
  <dc:description xml:lang="eng">Abstract:
This study tackles the Garden of Forking Paths, as a challenge for replicability and
reproducibility of ERP studies. Here, we applied a multiverse analysis to a sample
ERP N400 dataset, donated by an independent research team. We analyzed this
dataset using 14 pipelines selected to showcase the full range of methodological
variability found in the N400 literature using systematic review approach. The
selected pipelines were compared in depth by looking into statistical test out-
comes, descriptive statistics, effect size, data quality, and statistical power. In this
way we provide a worked example of how analytic flexibility can impact results
in research fields with high dimensionality such as ERP, when analyzed using
standard null-hypothesis significance testing. Out of the methodological deci-
sions that were varied, high-pass filter cut-off, artifact removal method, baseline
duration, reference, measurement latency and locations, and amplitude measure
(peak vs. mean) were all shown to affect at least some of the study outcome meas-
ures. Low-pass filtering was the only step which did not notably influence any of
these measures. This study shows that even some of the seemingly minor proce-
dural deviations can influence the conclusions of an ERP study. We demonstrate
the power of multiverse analysis in both identifying the most reliable effects in
a given study, and for providing insights into consequences of methodological
decisions.
</dc:description>
  <dc:description xml:lang="eng">https://onlinelibrary.wiley.com/doi/10.1111/psyp.14628</dc:description>
</oai_dc:dc>
