Mental chronometry in big noisy data
Temporal measures (latencies) in the event-related potentials of the EEG (ERPs) are a valuable tool for estimating the timing of mental processes, one which takes full advantage of the high temporal resolution of the EEG. Especially in larger scale studies using a multitude of individual EEG-based t...
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Published in | PloS one Vol. 17; no. 6; p. e0268916 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
08.06.2022
Public Library of Science (PLoS) |
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Online Access | Get full text |
ISSN | 1932-6203 1932-6203 |
DOI | 10.1371/journal.pone.0268916 |
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Abstract | Temporal measures (latencies) in the event-related potentials of the EEG (ERPs) are a valuable tool for estimating the timing of mental processes, one which takes full advantage of the high temporal resolution of the EEG. Especially in larger scale studies using a multitude of individual EEG-based tasks, the quality of latency measures often suffers from high and low frequency noise residuals due to the resulting low trial counts (because of compressed tasks) and because of the limited feasibility of visual inspection of the large-scale data. In the present study, we systematically evaluated two different approaches to latency estimation (peak latencies and fractional area latencies) with respect to their data quality and the application of noise reduction by jackknifing methods. Additionally, we tested the recently introduced method of Standardized Measurement Error (SME) to prune the dataset. We demonstrate that fractional area latency in pruned and jackknifed data may amplify within-subjects effect sizes dramatically in the analyzed data set. Between-subjects effects were less affected by the applied procedures, but remained stable regardless of procedure. |
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AbstractList | Temporal measures (latencies) in the event-related potentials of the EEG (ERPs) are a valuable tool for estimating the timing of mental processes, one which takes full advantage of the high temporal resolution of the EEG. Especially in larger scale studies using a multitude of individual EEG-based tasks, the quality of latency measures often suffers from high and low frequency noise residuals due to the resulting low trial counts (because of compressed tasks) and because of the limited feasibility of visual inspection of the large-scale data. In the present study, we systematically evaluated two different approaches to latency estimation (peak latencies and fractional area latencies) with respect to their data quality and the application of noise reduction by jackknifing methods. Additionally, we tested the recently introduced method of Standardized Measurement Error (SME) to prune the dataset. We demonstrate that fractional area latency in pruned and jackknifed data may amplify within-subjects effect sizes dramatically in the analyzed data set. Between-subjects effects were less affected by the applied procedures, but remained stable regardless of procedure. Temporal measures (latencies) in the event-related potentials of the EEG (ERPs) are a valuable tool for estimating the timing of mental processes, one which takes full advantage of the high temporal resolution of the EEG. Especially in larger scale studies using a multitude of individual EEG-based tasks, the quality of latency measures often suffers from high and low frequency noise residuals due to the resulting low trial counts (because of compressed tasks) and because of the limited feasibility of visual inspection of the large-scale data. In the present study, we systematically evaluated two different approaches to latency estimation (peak latencies and fractional area latencies) with respect to their data quality and the application of noise reduction by jackknifing methods. Additionally, we tested the recently introduced method of Standardized Measurement Error (SME) to prune the dataset. We demonstrate that fractional area latency in pruned and jackknifed data may amplify within-subjects effect sizes dramatically in the analyzed data set. Between-subjects effects were less affected by the applied procedures, but remained stable regardless of procedure.Temporal measures (latencies) in the event-related potentials of the EEG (ERPs) are a valuable tool for estimating the timing of mental processes, one which takes full advantage of the high temporal resolution of the EEG. Especially in larger scale studies using a multitude of individual EEG-based tasks, the quality of latency measures often suffers from high and low frequency noise residuals due to the resulting low trial counts (because of compressed tasks) and because of the limited feasibility of visual inspection of the large-scale data. In the present study, we systematically evaluated two different approaches to latency estimation (peak latencies and fractional area latencies) with respect to their data quality and the application of noise reduction by jackknifing methods. Additionally, we tested the recently introduced method of Standardized Measurement Error (SME) to prune the dataset. We demonstrate that fractional area latency in pruned and jackknifed data may amplify within-subjects effect sizes dramatically in the analyzed data set. Between-subjects effects were less affected by the applied procedures, but remained stable regardless of procedure. |
Audience | Academic |
Author | Schneider, Daniel Gutberlet, Marie Getzmann, Stephan Arnau, Stefan Sharifian, Fariba Wascher, Edmund |
AuthorAffiliation | Dept. Ergonomics, IfADo–Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany National University of Sciences and Technology, PAKISTAN |
AuthorAffiliation_xml | – name: National University of Sciences and Technology, PAKISTAN – name: Dept. Ergonomics, IfADo–Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany |
Author_xml | – sequence: 1 givenname: Edmund surname: Wascher fullname: Wascher, Edmund – sequence: 2 givenname: Fariba surname: Sharifian fullname: Sharifian, Fariba – sequence: 3 givenname: Marie surname: Gutberlet fullname: Gutberlet, Marie – sequence: 4 givenname: Daniel surname: Schneider fullname: Schneider, Daniel – sequence: 5 givenname: Stephan surname: Getzmann fullname: Getzmann, Stephan – sequence: 6 givenname: Stefan surname: Arnau fullname: Arnau, Stefan |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35675345$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_1016_j_heares_2024_108968 crossref_primary_10_1038_s41598_024_71691_x crossref_primary_10_1111_psyp_14459 crossref_primary_10_1016_j_neurobiolaging_2023_02_003 crossref_primary_10_1016_j_ijpsycho_2024_112311 crossref_primary_10_1016_j_neurobiolaging_2024_09_012 crossref_primary_10_3389_fpsyg_2023_1137698 crossref_primary_10_1111_psyp_14165 |
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Copyright | COPYRIGHT 2022 Public Library of Science 2022 Wascher et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 Wascher et al 2022 Wascher et al |
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Title | Mental chronometry in big noisy data |
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