For the single-trial variability (STV) fMRI analysis, we modeled the variability of the neural response using an additional two regressors – one each for targets and standards. These EEG-based regressors were designed with duration 100 ms, centered on the classifier training window. The STV regressor height was modulated using the output yτ,i of the EEG discriminator for each trial (i). These regressors were convolved with the HRF and orthogonalized with respect to all traditional regressors, with temporal derivatives included as confounds. It was especially important to regress out the RT variability, since RT is known to be negatively correlated with attention (Eason et al., 1969; Weissman et al., 2006), and our aim was to study variability in task-engagement that cannot be detected using an external measure. This entire analysis was run independently for all stimulus-locked and response-locked EEG training windows exceeding a mean Az value of 0.75, which is a common psychophysical threshold used in signal detection theory and here represents substantial performance of the classifier. To avoid stimulus-type confounds, we focused on within-class variability, using only the target stimuli STV statistical maps in our results interpretation.
