The impact of novelty and emotion on attention-related neuronal and pupil responses in children

Bonmassar, Carolina; Widmann, Andreas; Wetzel, Nicole

doi:10.1016/j.dcn.2020.100766

Cited by 34 publications

(69 citation statements)

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“…It is not entirely clear yet whether P3a represent the same underlying processes in passive and active listening situations [24]. Actually, in the direct comparison of passive and active listening task (Fig 5), it appears that P3a elicited by true pattern changes might be separable into two distinct subcomponents [106][107][108][109][110]-namely an early and a late P3a. When visually comparing both studies a potential late P3a was elicited of similar latency and amplitude in both studies irrespective of pitch information.…”

Section: Plos Onementioning

confidence: 95%

Change detection of auditory tonal patterns defined by absolute versus relative pitch information. A combined behavioural and EEG study

et al. 2021

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The human auditory system often relies on relative pitch information to extract and identify auditory objects; such as when the same melody is played in different keys. The current study investigated the mental chronometry underlying the active discrimination of unfamiliar melodic six-tone patterns by measuring behavioural performance and event-related potentials (ERPs). In a roving standard paradigm, such patterns were either repeated identically within a stimulus train, carrying absolute frequency information about the pattern, or shifted in pitch (transposed) between repetitions, so only relative pitch information was available to extract the pattern identity. Results showed that participants were able to use relative pitch to detect when a new melodic pattern occurred. Though in the absence of absolute pitch sensitivity significantly decreased and behavioural reaction time to pattern changes increased. Mismatch-Negativity (MMN), an ERP indicator of auditory deviance detection, was elicited at approximately 206 ms after stimulus onset at frontocentral electrodes, even when only relative pitch was available to inform pattern discrimination. A P3a was elicited in both conditions, comparable in amplitude and latency. Increased latencies but no differences in amplitudes of N2b, and P3b suggest that processing at higher levels is affected when, in the absence of absolute pitch cues, relative pitch has to be extracted to inform pattern discrimination. Interestingly, the response delay of approximately 70 ms on the behavioural level, already fully manifests at the level of N2b. This is in accordance with recent findings on implicit auditory learning processes and suggests that in the absence of absolute pitch cues a slowing of target selection rather than a slowing of the auditory pattern change detection process causes the deterioration in behavioural performance.

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Section: Plos Onementioning

confidence: 95%

Change detection of auditory tonal patterns defined by absolute versus relative pitch information. A combined behavioural and EEG study

et al. 2021

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“…The factors which were in the focus of the original publication were already highlighted by thicker lines in Figure 8 (Bonmassar et al, 2020). However, until now, we only know that these factors are active in the time range relevant for the research questions.…”

Section: Unstandardized Factor Loadings After Geomin (001) Rotation In Both Efasmentioning

confidence: 96%

“…In the following, we analyze an ERP dataset that was previously analyzed and reported by Bonmassar et al (2020). We set out from a pre-processed version of the participant-level average data (i.e., already averaged over trials) exported from MATLAB (avrdata.mat).…”

Section: Prerequisitesmentioning

confidence: 99%

“…They focused on the ERP components P2, early and late P3a, and the Late Discriminative Negativity (LDN). These components are discussed to reflect mechanisms involved in stimulus classification, inhibition, orienting and reorienting of attention in the framework of distraction of attention by unexpected and task-irrelevant sounds (for details see, Bonmassar et al, 2020). For the sake of comprehensibility, we will only reproduce selected original results in order to illustrate the specific analysis steps in the application of a temporal EFA.…”

Section: Prerequisitesmentioning

confidence: 99%

“…Note that the dataset is not equivalent to Bonmassar et al (2020) because the preprocessing differed in some aspects. Specifically, the following details differed: (1) We did not reject data due to artifacts in the co-registered pupil data, (2) the data were filtered with a 48 Hz low-pass filter (length: 414 points), (3) bad channels were excluded and interpolated when they exceeded a robust z-score of 3 of the robust standard deviation of the channels (Bigdely-Shamlo et al, 2015), (4) epochs exceeding amplitude differences of 750 µV (based on 0.1 Hz high-pass and low-pass filtered data) were excluded before ICA to remove non-stereotypical artifacts, and (5) the ICA was re-computed using AMICA (Delorme et al, 2012) on the 1 Hz high-pass filtered data without baseline correction (Klug & Gramann, 2020).…”

Section: Prerequisitesmentioning

confidence: 99%

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A tutorial on the use of temporal exploratory factor analysis in developmental ERP research – chances and challenges

Scharf¹,

Widmann²,

Bonmassar³

et al. 2021

Preprint

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Developmental researchers are often interested in event-related potentials (ERPs) as a measure of brain activity occurring time-locked to an event of interest. The traditional data-analytic approach for ERPs is based on the observed ERP and suffers from two major problems: First, the definition of analysis time windows as well as regions of interest often proceeds in a relatively arbitrary manner. Second, the observed signal at the scalp is a mixture of underlying signals generated in the brain. The temporal exploratory factor analysis (EFA) for ERP data, also known as temporal principal component analysis (PCA), aims to address these problems. However, its application in developmental ERP research settings comes with the unique challenge that the component structure differs between children and adults (so-called measurement non-invariance). Separate EFAs for the groups can be used to cope with this challenge and gain valuable insights into developmental processes. Here, we demonstrate how to make results from separate EFAs accessible for inferential statistics by re-scaling to original units. In addition, separate EFAs enable analyses of latency differences between groups. We propose the application of an established Jackknife approach to the factor solution for this purpose. With this tutorial, we want to enable readers with a focus on developmental research to conduct an EFA-based ERP analysis of amplitude and latency differences to address their research questions. We explain the benefits of an EFA-based approach, introduce the EFA model and demonstrate its application to a developmental research question step-by-step using real-data from a child and an adult group (code and data are made available). Finally, we discuss how to cope with typical challenges during the analysis and name potential limitations.

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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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The impact of novelty and emotion on attention-related neuronal and pupil responses in children

Cited by 34 publications

References 100 publications

Change detection of auditory tonal patterns defined by absolute versus relative pitch information. A combined behavioural and EEG study

Change detection of auditory tonal patterns defined by absolute versus relative pitch information. A combined behavioural and EEG study

A tutorial on the use of temporal exploratory factor analysis in developmental ERP research – chances and challenges

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

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