N-Back Related ERPs Depend on Stimulus Type, Task Structure, Pre-processing, and Lab Factors

Mahsa Alizadeh Shalchy, Valentina Pergher, Anja Pahor, Marc M Van Hulle, Aaron R Seitz, Mahsa Alizadeh Shalchy, Valentina Pergher, Anja Pahor, Marc M Van Hulle, Aaron R Seitz

Abstract

The N-Back, a common working memory (WM) updating task, is increasingly used in basic and applied psychological research. As such, an increasing number of electroencephalogram (EEG) studies have sought to identify the electrophysiological signatures of N-Back task performance. However, stimulus type, task structure, pre-processing methods, and differences in the laboratory environment, including the EEG recording setup employed, greatly vary across studies, which in turn may introduce inconsistencies in the obtained results. Here we address this issue by conducting nine different variations of an N-Back task manipulating stimulus type and task structure. Furthermore, we explored the effect of the pre-processing method used and differences in the laboratory environment. Results reveal significant differences in behavioral and electrophysiological signatures in response to N-Back stimulus type, task structure, pre-processing method, and laboratory environment. In conclusion, we suggest that experimental factors, analysis pipeline, and laboratory differences, which are often ignored in the literature, need to be accounted for when interpreting findings and making comparisons across studies.

Keywords: ERPs; N-Back; cross-laboratory; experimental features; pre-processing; working memory.

Copyright © 2020 Alizadeh Shalchy, Pergher, Pahor, Van Hulle and Seitz.

Figures

Figure 1
Figure 1
Graphic rendition of N-Back task features for stimulus type, stimulus duration, and Inter-stimulus Interval (ISI) for Dataset I.
Figure 2
Figure 2
Mean accuracy and SEM for target trials in the University of California—Riverside (UCR) dataset. (A) Accuracy as a function of task type. (B) Accuracy as a function of stimulus type. *Indicates the significance of p < 0.05.
Figure 3
Figure 3
Grand average and SEM of ERP curve for UCR dataset at Cz electrode for target trials during variations of stimulus types (words, pictures, and colors). Gray shaded areas indicate significantly different data points (p < 0.05). P-values that are less than 0.0001 are thresholded to 0.0001 for viewing purposes, as shown by the black curve at the bottom of each graph where log p-values are reported.
Figure 4
Figure 4
Grand average and SEM of ERP curve for UCR dataset at Cz electrode for target trials during variations of task structure (task 1, task 2, and task 3). Gray shaded areas indicate significantly different data points (p < 0.05). P-values that are less than 0.0001 are thresholded to 0.0001 for viewing purposes.
Figure 5
Figure 5
Grand average and SEM of ERP curve for UCR dataset at Cz electrode as a function of N-back load (A) and performance metrics (B).
Figure 6
Figure 6
Grand average and SEM of ERP curve at Cz electrode for target trials for different pipelines (Pipeline I vs. Pipeline II) for the UCR dataset (see Supplementary Material for Fz and Pz, Supplementary Figures 7, 8). Gray shaded areas indicate significantly different data points (p < 0.05). P-values that are less than 0.0001 are thresholded to 0.0001 for viewing purposes. Data in Pipeline II was up-sampled to 512 to make the comparison possible.
Figure 7
Figure 7
Cross-laboratory accuracy comparison. (A) Accuracy for N-Back task 2 with pictures in the dataset I (UCR) and in dataset II (Ku Leuven). (B) Accuracy for N-Back task 1 with words in the dataset I (UCR) and in dataset III (University of Maribor, UM). *Indicates significance of p < 0.05.
Figure 8
Figure 8
ERP responses during task 2, only for target stimuli recorded at different laboratories. Gray shaded areas show significant differences at p < 0.05. Both datasets were pre-processed with pipeline I.
Figure 9
Figure 9
ERP responses during task 1 (mean and standard deviation of targets) recorded at different laboratories. Gray shaded areas show significant differences at p < 0.05. Both datasets were pre-processed with pipeline I.

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