Mental chronometry in big noisy data.
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2022
2022
Historique:
received:
29
11
2021
accepted:
11
05
2022
entrez:
8
6
2022
pubmed:
9
6
2022
medline:
11
6
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35675345
doi: 10.1371/journal.pone.0268916
pii: PONE-D-21-37772
pmc: PMC9176764
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0268916Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
Références
Trends Cogn Sci. 2004 May;8(5):204-10
pubmed: 15120678
Front Neurosci. 2018 Feb 27;12:97
pubmed: 29535597
Psychophysiology. 1993 Nov;30(6):547-58
pubmed: 8248447
Psychophysiology. 2010 Mar 1;47(2):387-92
pubmed: 20003147
Brain Cogn. 2004 Oct;56(1):5-13
pubmed: 15380870
Psychophysiology. 2014 Jan;51(1):1-21
pubmed: 24147581
Atten Percept Psychophys. 2010 Nov;72(8):2031-46
pubmed: 21097848
Psychophysiology. 2021 Jun;58(6):e13793
pubmed: 33782996
Psychophysiology. 2008 Jan;45(1):152-70
pubmed: 17850238
PLoS Biol. 2005 Feb;3(2):e51
pubmed: 15719059
Front Neuroinform. 2015 Jun 18;9:16
pubmed: 26150785
Electroencephalogr Clin Neurophysiol. 1980 Aug;49(3-4):277-90
pubmed: 6158404
Neuropsychologia. 1987;25(1A):85-96
pubmed: 3574653
Psychophysiology. 1989 May;26(3):251-69
pubmed: 2667018
Psychophysiology. 2000 Mar;37(2):127-52
pubmed: 10731765
Psychophysiology. 1998 Jan;35(1):99-115
pubmed: 9499711
Int J Psychophysiol. 2020 Jul;153:107-115
pubmed: 32376160
Psychophysiology. 2018 Jun;55(6):e13049
pubmed: 29266241
Psychophysiology. 2020 Jul;57(7):e13542
pubmed: 32030756
Psychophysiology. 2001 Sep;38(5):816-27
pubmed: 11577905
Neuroimage. 2019 Sep;198:181-197
pubmed: 31103785
Proc Natl Acad Sci U S A. 1998 Feb 3;95(3):781-7
pubmed: 9448241
Biol Psychol. 1988 Jun;26(1-3):3-67
pubmed: 3061480
Neuroimage. 2013 Oct 15;80:169-89
pubmed: 23684877
Front Neurosci. 2018 Oct 25;12:765
pubmed: 30410431
Psychophysiology. 2008 Mar;45(2):250-74
pubmed: 17995913
Front Neurosci. 2021 Jun 07;15:660449
pubmed: 34163321
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:4101-5
pubmed: 26737196
JMIR Res Protoc. 2022 Mar 14;11(3):e32352
pubmed: 35285810
Psychophysiology. 2009 Mar;46(2):300-12
pubmed: 19207197
J Neurosci Methods. 2004 Mar 15;134(1):9-21
pubmed: 15102499