-
Notifications
You must be signed in to change notification settings - Fork 3
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #10 from Inria-Empenn/hugoblox-import-publications
Hugo Blox Builder - Import latest publications
- Loading branch information
Showing
2 changed files
with
51 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,13 @@ | ||
@article{soskic_garden_nodate, | ||
abstract = {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 outcomes, 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 decisions 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 measures. 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 procedural 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.}, | ||
author = {Šoškić, Anđela and Styles, Suzy J. and Kappenman, Emily S. and Ković, Vanja}, | ||
doi = {https://doi.org/10.1111/psyp.14628}, | ||
journal = {Psychophysiology}, | ||
keywords = {to read}, | ||
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/psyp.14628}, | ||
number = {n/a}, | ||
pages = {e14628}, | ||
title = {Garden of forking paths in ERP research: Effects of varying pre-processing and analysis steps in an N400 experiment}, | ||
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/psyp.14628}, | ||
volume = {n/a} | ||
} |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,38 @@ | ||
--- | ||
title: 'Garden of forking paths in ERP research: Effects of varying pre-processing | ||
and analysis steps in an N400 experiment' | ||
authors: | ||
- Anđela Šoškić | ||
- Suzy J. Styles | ||
- Emily S. Kappenman | ||
- Vanja Ković | ||
date: -01-01 | ||
publishDate: '2024-07-24T13:16:21.318117Z' | ||
publication_types: | ||
- article-journal | ||
publication: '*Psychophysiology*' | ||
doi: https://doi.org/10.1111/psyp.14628 | ||
abstract: 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 outcomes, 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 decisions | ||
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 measures. 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 procedural 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. | ||
tags: | ||
- to read | ||
links: | ||
- name: URL | ||
url: https://onlinelibrary.wiley.com/doi/abs/10.1111/psyp.14628 | ||
--- |