2.3 EEG acquisition and ERP quantification
Continuous EEGs were recorded from 32 scalp sites using Ag/AgCl electrodes embedded in elastic caps (Brain Products, Germany) according to the international 10–20 system. Online recordings were referenced to the right mastoid. Electrode impedance were kept below 10 kΩ. The sampling rate was set to 1000 Hz.
For offline analyses, all data were processed with BrainVision Analyzer software (Brain Products, Germany). Continuous EEGs were filtered with a 30Hz low-pass filter and a 0.1Hz high-pass filter. Ocular Correction ICA was used to remove eye movements. Continuous EEGs were then segmented into 1,200 ms epochs and were time-locked to either the probe/irrelevant stimuli or to the positive/negative feedback stimuli. The 1,200 ms epoch contained a 200 ms pre-stimulus baseline, and a 1,000 ms time window after stimulus onset. The irrelevant ERPs were averaged across all four irrelevant items. Trials with signals exceeding 100 μV were defined as artifacts and excluded from averaging.
For ERPs time-locked to CIT stimuli (i.e., probe vs. irrelevant), we focused on the P300 component at Pz which often occurs between 350 - 550 ms (Matsuda & Nittono, 2018; Matsuda, Nittono, Hirota, Ogawa, & Takasawa, 2009), the P200 component at FCz which often occurs (between 150 - 250 ms) (Tacikowski, Cygan, & Nowicka, 2014; Xu et al., 2011), and the N200 component at FCz which often occurs between 250 - 350 ms (Ganis, Bridges, Hsu, & Schendan, 2016; Hu, Wu, & Fu, 2011). These recognition-related components were quantified using temporal PCA, a technique that extracts linear combinations of data points meeting certain criteria which tend to distinguish between consistent patterns of electrocortical activity (Dien and Frishkoff, 2005). This analysis was conducted using the ERP PCA Toolkit, version 2.86 (Dien, 2010). This PCA used 1200 time points (200 ms before CIT-stimuli onset served as baseline) from each participant’s averaged ERP as variables, with participants and conditions as observations. Promax rotation was used (Dien, Khoe, & Mangun, 2007), and eleven temporal factors were extracted based on the resulting scree plot (Cattell, 1966). Of these factors, nine factors accounted for more than 1% of the total variance in the data. We selected the positive component peaking at 384 ms as the recognition-P300, the positivity component at 206 ms as the P200, and the negativity component at 316 ms as the N200 for further calculation (see Table 1).
For feedback-locked ERPs, we concentrated on the FRN at Fz which occurs 250-350 ms and the feedback-P300 at Pz which occurs 350-550ms (Foti, Weinberg, Dien, & Hajcak, 2011; Sai et al., 2016 ). Since the FRN and the feedback-P300 are largely overlapped, we also conducted a temporal PCA to analyze them. This PCA was performed with promax rotation, using 1200 time points (200 ms before feedback onset served as the baseline) from each participant’s averaged ERP as variables, with participants and conditions as observations. Based on scree plots, 13 factors were extracted and 6 factors accounted for more than 1% of the total variance in the data. We selected the positive component peaking at 282 ms as the FRN and the positivity component at 424 ms as the feedback P300 for further calculation (see Table 1 and Table S1). The waveforms for each factor were reconstructed (i.e., converted to microvolts) by multiplying the factor pattern matrix with the standard deviations. These factors were scored using the peak values (Foti et al., 2011), which were applied to subsequent analyses, including analyses of variance (ANOVA), and ROC analyses.
All analyses were conducted using SPSS 20.0. For ANOVA, the Greenhouse-Geisser correction was applied when the assumption of sphericity was violated. Post-hoc comparisons were computed with Fisher’s Protected Least Significant Difference. Effect size was estimated by the partial eta squared valueηp2 .
In addition, Bayes factors (JZS BFs, with scaled r  = 0.707, as in Rouder, Speckman, Sun, Morey, & Iverson (2009) were reported as a complement to classical statistics. The BFs serve as a method of quantifying the ratio of the likelihood of the null hypothesis to the likelihood of the alternative hypothesis. The ratio will be stated as favoring either the null hypothesis (no difference) or the alternative hypothesis (Jeffreys, 1998; Kass & Raftery, 1995). For t -tests, the BF10 (favoring the alternative hypothesis) or the BF01 (favoring the null hypothesis) is reported. For ANOVA effects, the BFInclusion (favoring the alternative hypothesis) or BFExclusion (favoring the null hypothesis) is reported, reflecting a comparison of all models containing a particular effect to those without the effect (also see Klein Selle, Gueta, Harpaz, Deouell, & Ben-Shakhar, 2021). A BF value of ≥ 3 was regarded as moderate evidence for the respective hypothesis (Kass & Raftery, 1995). BFs were computed using JASP (Version 0.14.1, https://jasp-stats.org/).