Data processing was conducted in MATLAB (The Mathworks, Natick, MA) using identical methods for both systems, based on functions in the Psychophysiology Toolbox http://sourceforge.net/projects/psychophys/ and custom scripts. BioSemi data were transformed to be comparable to the original data, as described in the online supporting information. Notes recorded at the time the data were collected guided us in visually identifying problematic data that might need to be excluded. In addition, specific trials containing transient artifacts and excessively small or large voltage deflections were tagged for exclusion by computer algorithm. To increase reliability of peak selection, target ERPs were low-pass filtered with a cutoff frequency of 10 Hz using a finite impulse response (FIR) filter with least-squared error to minimize the contribution of higher frequencies (Losada, 2004). A computer algorithm selected the largest peak within a window between 300 and 600 ms as the P3. Outliers with respect to amplitude and latency were identified and their data visually screened to determine whether outlyingness was due to problems necessitating subject exclusion. In addition, we identified multivariate outliers using the three parietal electrodes and
