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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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0268916&type=printable |
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| author | Edmund Wascher Fariba Sharifian Marie Gutberlet Daniel Schneider Stephan Getzmann Stefan Arnau |
| author_facet | Edmund Wascher Fariba Sharifian Marie Gutberlet Daniel Schneider Stephan Getzmann Stefan Arnau |
| author_sort | Edmund Wascher |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-99c0aff2e34e4475957c8157ecbdf477 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-99c0aff2e34e4475957c8157ecbdf4772025-08-20T03:16:35ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01176e026891610.1371/journal.pone.0268916Mental chronometry in big noisy data.Edmund WascherFariba SharifianMarie GutberletDaniel SchneiderStephan GetzmannStefan ArnauTemporal 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0268916&type=printable |
| spellingShingle | Edmund Wascher Fariba Sharifian Marie Gutberlet Daniel Schneider Stephan Getzmann Stefan Arnau Mental chronometry in big noisy data. PLoS ONE |
| title | Mental chronometry in big noisy data. |
| title_full | Mental chronometry in big noisy data. |
| title_fullStr | Mental chronometry in big noisy data. |
| title_full_unstemmed | Mental chronometry in big noisy data. |
| title_short | Mental chronometry in big noisy data. |
| title_sort | mental chronometry in big noisy data |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0268916&type=printable |
| work_keys_str_mv | AT edmundwascher mentalchronometryinbignoisydata AT faribasharifian mentalchronometryinbignoisydata AT mariegutberlet mentalchronometryinbignoisydata AT danielschneider mentalchronometryinbignoisydata AT stephangetzmann mentalchronometryinbignoisydata AT stefanarnau mentalchronometryinbignoisydata |