2.4 Data recording and analyses
Electrophysiological data were recorded using 64-channel caps with Ag/AgCI impedance-optimized active electrodes. Standard electrodes were placed according to the extended 10-20 positioning system, and impedances were kept below 5 kΩ. The continuous signal was recorded with a sampling rate of 1000 Hz and referenced online to the electrode placed over the Cz. Using EEGLAB (Brunner et al., 2013; Delorme & Makeig, 2004; Makeig et al., 1995) for data processing, EEG data was down-sampled to 500 Hz offline and filtered online within a high-pass filter for .1 Hz and a low-pass filter for 30 Hz. All electrode sites were rereferenced offline to the average of the left and right mastoids. Reduction of ocular movement artifacts was performed through Independent Component Analysis (ICA) rejection (Makeig et al., 1995; Delorme & Makeig, 2004). The mean number of ICs rejected was 1.66 ± .91 per participant (natural: 1.65 ± .71; forced: 1.79 ± .89; voluntary: 1.56 ± 1.11). The continuous recordings were cut into epochs ranging from -200 to 800 ms relative to the onset of each flanker trial. Baseline correction was performed in reference to pre-stimulus activity (-200 to 0 ms). We subjected all epochs to a rejection procedure in which epochs containing deviations larger than 100 μV were discarded. This procedure rejected 670 epochs (rejection rate: .17%), and the mean rejection rate per participant was 1.73%.
ERP components were defined based on previous literature and visual inspection of wave-forms. Two time-windows were used to explore the components of interest. Firstly, a 120-220 ms time-window classically defined as N1 was selected. Secondly, a 350-450 ms time-window was chosen to analyze the P3 component. In accordance with prior studies (Christoffels et al., 2007; Jackson et al., 2001; Martin et al., 2013; Verhoef et al., 2010), we focused our analyses on two regions of interest (ROIs): central-parietal: CP3, CP1, CPz, CP2, CP4; and parietal: P3, P1, Pz, P2, P4. The N1 effect is assumed to be indicative of persistent covert visual attention control and sensitive to the level of visual attention intensity on the target stimuli (Di Russo). The greater N1 amplitude for incongruent relative to congruent trials implys more attention allocated to a given stimuli (Luck et al., 2000). Traditionally, a parietal P3 component was thought to index context updating operations, demonstrating the late, top-down controlled process associated with the allocation of attentional resources and updating of working memory (Donchin & Coles, 1998; Duncan-Johnson & Donchin, 1977; Polich, 2007; Liu et al., 2023b). In flanker tasks, the P3 effect has often been found to be enhanced and delayed on incongruent trials compared to congruent trials (Folstein et al., 2008).
Response times (RTs) and accuracy on each flanker trial were recorded. We excluded the first two warm-up trials of each block and RTs ±2.5SD (.64% of the total) from the analyses. In addition, since accuracy rates of flanker trials was 97.88% (SD = .14), and there was only a significant main effect for trial type, we did not conduct further analyses on accuracy rates. In total, the remaining 96.07% trials were included in the RT analyses. RTs were log-transformed to better approximate a normal distribution.
Statistical analyses were conducted in R using the lme4 package (Bates et al., 2015). The RT values and single-trial mean amplitude were submitted separately to 3 × 2 × 2 linear mixed-effects models. The fixed effects included “context” (natural vs. forced vs. voluntary), “trial type” (switch vs. non-switch), and “flanker” (congruent vs. incongruent), and interactions between these factors; and random effects in the RT model included participants and items, whereas only participants were added as a random effect of the model on single-trial mean amplitude. We started with a full model including the structure of maximal random effects (Barr et al., 2013), and added the three factors and their interactions as random slopes. If the model failed to converge, we used a backwards-stepping procedure until the model fit. The decision to include or exclude random slope effects of each model was based on the smallest Akaike Information Criterion (AIC) value, and the model with the smallest AIC was selected as the best-fitting model. Because “context” in the model involved three levels, any significant main effects or interactions were further analyzed by comparing the estimated means using the “emmeans” package. Moreover, we performed pairwise comparisons between flanker trials and each context to identify any differences. The p -values obtained were adjusted for multiple comparisons using false discovery rate (FDR) correction. Given the aims of the study, we only present the effects involving the factors “flanker” and/or “context.”