2.5 Prediction model
We utilized a family of regularized Ridge Regression (RR) models to predict brain activity during the immediate flanker task using the EEG data collected during the preceding picture-naming trials. RR is a widely employed parameter estimation technique for addressing linear regression challenges characterized by multicollinearity, offering robustness against noise (Engemann et al., 2020; Meir-Hasson et al., 2014). Following EEG data preprocessing during language processing to align with flanker task standards, a series of RR models were implemented. Initially, 48 electrodes were retained and averaged after removal of electrodes with high impedance from the outer circle. Subsequently, EEG activity for each participant across language-switching contexts (forced, natural, and voluntary switching) and subsequent flanker tasks (congruent, incongruent) was isolated for each trial, condition (switch-congruent, switch-incongruent, non-switch-congruent, non-switch-incongruent), and time point (within the 0-500 ms window).
Due to the absolute autonomy of language selection in the voluntary switching context, the trial count under each condition was not uniform. Thus, averaging was performed across the trial dimensions for the three contexts, resulting in a three-dimensional matrix of 4 (conditions) × 29 (participants) × 500 (time points). Next, the condition and participant dimensions were merged to yield a matrix of 116 × 500, with the initial 58 × 500 serving as the training set and the subsequent 58 × 500 constituting the test set. Model training involved employing bootstrapping to determine the optimal alpha value for each response. Specifically, 50 of the 58 matrix data were randomly sampled from the training set for model training, and the remaining 8 were used for validation. Fifteen rounds of cross-validation were executed to ascertain the best alpha value for each response. Upon determining the optimal alpha value, model weights were acquired. Utilizing the language task test set 58 × 500 (independent variable X ) as input and the weight multiplication of the trained model, the predicted EEG signal of the flanker task (i.e., the predicted value, ŷ ) was obtained. To validate the fit of the model, Pearson’s correlation analyses were conducted between the predicted brain activity (i.e., ŷ ) during the flanker task and the actual amplitude data (i.e., y ) of the flanker task at each time point. A higher r -value indicates better prediction performance. In essence, utilizing coding activity from language-switching tasks as the independent variable X , we predicted the corresponding processing of the flanker tasks (i.e., dependent variable y ), yielding ridge correlation coefficients. These coefficients, reflecting the correlation between language processing and executive control processing, address the second question of this study: the influence of different switching modes on executive control, particularly which type of executive advantage predominates.