Charting development of ERP components on face-categorization: Results from a large longitudinal sample of infants

Lorenzo, Renata Di; Boomen, Carlijn van den; Kemner, Chantal; Junge, Caroline

doi:10.1016/j.dcn.2020.100840

Cited by 11 publications

(21 citation statements)

References 55 publications

(130 reference statements)

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“…Alternatively, the N290/N170 and P400 components are associated with cognitive processing and face recognition and may be observed across a broader time window (particularly the P400). Recent work providing evidence of N290 latency variability across testing sessions(Di Lorenzo et al, 2020) provides further support for the potential of the ReSync program in increasing the accuracy of measurement of this component.…”

mentioning

confidence: 85%

“…Amplitude and latency of the P1 are believed to reflect low-level processing of visual stimuli. The N290 is believed to be the precursor to the adult and child N170 and, like the N170, is greater in amplitude to face than nonface visual stimuli (Conte et al, 2020;Di Lorenzo et al, 2020;Guy et al, 2018). Although the P400 has been investigated in several studies of visual information processing, the functional role of the P400 is still unclear.…”

Section: Semiautomated and Individualized Time Window Selectionmentioning

confidence: 99%

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Peak selection and latency jitter correction in developmental event‐related potentials

Guy

Conte

Bursalıoğlu

et al. 2021

Developmental Psychobiology

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Event-related potentials (ERPs) provide great insight into neural responses, yet developmental ERP work is plagued with inconsistent approaches to identifying and quantifying component latency. In this analytical review, we describe popular conventions for the selection of time windows for ERP analysis and assert that a data-driven strategy should be applied to the identification of component latency within individual participants' data. This may overcome weaknesses of more general approaches to peak selection; however, it does not account for trial-by-trial variability within a participant. This issue, known as ERP latency jitter, may blur the average ERP, misleading the interpretation of neural mechanisms. Recently, the ReSync MATLAB toolbox has been made available for correction of latency jitter. Although not created specifically for pediatric ERP data, this approach can be adapted for developmental researchers. We have demonstrated the use of the ReSync toolbox with individual infant and child datasets to illustrate its utility. Details about our peak detection script and the ReSync toolbox are provided. The adoption of data processing procedures that allow for accurate, studyspecific component selection and reduce trial-by-trial asynchrony strengthens developmental ERP research by decreasing noise included in ERP analyses and improving the representation of the neural response.

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mentioning

confidence: 85%

Section: Semiautomated and Individualized Time Window Selectionmentioning

confidence: 99%

Peak selection and latency jitter correction in developmental event‐related potentials

Guy

Conte

Bursalıoğlu

et al. 2021

Developmental Psychobiology

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“…This null-hypothesis also concurs with other studies on different components. For instance, it appears that there is little change with ERP components specific to face-categorization (Di Lorenzo et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Development of the N400 for Word Learning in the First 2 Years of Life: A Systematic Review

et al. 2021

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The N400 ERP component is a direct neural index of word meaning. Studies show that the N400 component is already present in early infancy, albeit often delayed. Many researchers capitalize on this finding, using the N400 component to better understand how early language acquisition unfolds. However, variability in how researchers quantify the N400 makes it difficult to set clear predictions or build theory. Not much is known about how the N400 component develops in the first 2 years of life in terms of its latency and topographical distributions, nor do we know how task parameters affect its appearance. In the current paper we carry out a systematic review, comparing over 30 studies that report the N400 component as a proxy of semantic processing elicited in infants between 0 and 24 months old who listened to linguistic stimuli. Our main finding is that there is large heterogeneity across semantic-priming studies in reported characteristics of the N400, both with respect to latency and to distributions. With age, the onset of the N400 insignificantly decreases, while its offset slightly increases. We also examined whether the N400 appears different for recently-acquired novel words vs. existing words: both situations reveal heterogeneity across studies. Finally, we inspected whether the N400 was modulated differently with studies using a between-subject design. In infants with more proficient language skills the N400 was more often present or showed itself here with earlier latency, compared to their peers; but no consistent patterns were observed for distribution characteristics of the N400. One limitation of the current review is that we compared studies that widely differed in choice of EEG recordings, pre-processing steps and quantification of the N400, all of which could affect the characteristics of the infant N400. The field is still missing research that systematically tests development of the N400 using the same paradigm across infancy.

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“…To best approximate real ERP studies, we built the simulated data based on characteristics of real NC ERP data in existing developmental research. That is, we drew from the literature to determine population mean NC amplitude ( Leppänen et al, 2007 , Smith et al, 2020 ), age differences in NC mean amplitude ( Di Lorenzo et al, 2020 ), and NC amplitude decay across trials ( Borgström et al, 2016 ). We also modeled fixed and random effects commonly found in real ERP data such as condition differences and subject-level variability.…”

Section: Lme and Anova Comparison In Simulated Erp Datamentioning

confidence: 99%

Utility of linear mixed effects models for event-related potential research with infants and children

Heise

Mon

Bowman

2022

Developmental Cognitive Neuroscience

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Charting development of ERP components on face-categorization: Results from a large longitudinal sample of infants

Cited by 11 publications

References 55 publications

Peak selection and latency jitter correction in developmental event‐related potentials

Peak selection and latency jitter correction in developmental event‐related potentials

Development of the N400 for Word Learning in the First 2 Years of Life: A Systematic Review

Utility of linear mixed effects models for event-related potential research with infants and children

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