We first ran a traditional fMRI analysis, using event-related and RT variability regressors in our GLM. The event-related regressors were comprised of boxcar functions with unit amplitude and onset and offset matching that of the stimuli. RT variability was modeled using unit amplitude boxcars with onset at stimulus time and offset at response time, and these were orthogonalized to the event-related regressors. Orthogonalization was implemented using FSL, which utilizes the Gram-Schmidt procedure (Strang, 2003) to decorrelate the RT regressor from all other event-related regressors. All regressors were convolved with the canonical hemodynamic response function (HRF), and temporal derivatives were included as confounds of no interest. An event-related target vs. standards contrast was also constructed. A fixed effects model was used to model activations across runs, and a mixed effects approach used to compute the contrasts across subjects. Statistical image results for these traditional analyses were thresholded at z > 2.3, and clusters were multiple-comparison-corrected at p = 0.05 (Worsley, 2001).
