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. 2018 May;55(5):e13044.
doi: 10.1111/psyp.13044. Epub 2017 Dec 11.

Using trial-level data and multilevel modeling to investigate within-task change in event-related potentials

Hannah I Volpert-Esmond  1 , Edgar C Merkle  1 , Meredith P Levsen  1 , Tiffany A Ito  2 , Bruce D Bartholow  1
Affiliations

Using trial-level data and multilevel modeling to investigate within-task change in event-related potentials

Hannah I Volpert-Esmond et al. Psychophysiology. 2018 May.

Abstract

EEG data, and specifically the ERP, provide psychologists with the power to examine quickly occurring cognitive processes at the native temporal resolution at which they occur. Despite the advantages conferred by ERPs to examine processes at different points in time, ERP researchers commonly ignore the trial-to-trial temporal dimension by collapsing across trials of similar types (i.e., the signal averaging approach) because of constraints imposed by repeated measures ANOVA. Here, we present the advantages of using multilevel modeling (MLM) to examine trial-level data to investigate change in neurocognitive processes across the course of an experiment. Two examples are presented to illustrate the usefulness of this technique. The first demonstrates decreasing differentiation in N170 amplitude to faces of different races across the course of a race categorization task. The second demonstrates attenuation of the ERN as participants commit more errors within a task designed to measure implicit racial bias. Although the examples presented here are within the realm of social psychology, the use of MLM to analyze trial-level EEG data has the potential to contribute to a number of different theoretical domains within psychology.

Keywords: ERPs; analysis/statistical methods; error processing; face processing.

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Figures

Figure 1
Figure 1
The slopes associated with change in mean amplitude of the N170 across the course of the task are plotted here separately for Black and White male faces. Simple slopes and intercepts are obtained from Model B. Shaded areas reflect +/− 1 standard error in model predictions.
Figure 2
Figure 2
A) Grand average waveforms were formed using an average of P7, P8, TP7, and TP8 during the first and last 25% of trials. Negative amplitudes are plotted downward. Dashed lines indicate the interval in which N170 mean amplitude was quantified (135–195 ms). B) Mean amplitude of the N170 quantified from averaged waveforms for first and last 25% of trials. Error bars indicate standard error.
Figure 3
Figure 3
Grand average waveforms depicting the ERN for the first and last 25% of trials at fronto-central electrodes (averaged over Fz, FCz, Cz, F3, F4, FC3, FC4, C3, and C4) as a function of trial type, separately for correct and incorrect trials. Negative amplitudes are plotted upward, as is conventional when viewing the ERN.
Figure 4
Figure 4
The slopes associated with change in mean amplitude of the ERN across the course of the task are plotted here separately for different trial types. Simple slopes and intercepts are obtained from the model. Shaded areas reflect +/− 1 standard error in model predictions.
Figure 5
Figure 5
A) Grand average waveforms depicting the ERN for the first and last 25% of trials at fronto-central electrodes (averaged over Fz, FCz, Cz, F3, F4, FC3, FC4, C3, and C4) as a function of trial type (error trials only). B) Mean amplitude of ERN quantified from averaged waveforms (25–125 ms) for first and last 25% of trials. Error bars indicate standard error.
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