Resampling approach to statistical inference: Bootstrapping from event-related potentials data

We propose the use of the bootstrap resampling technique as a tool to asses s the within-subject reliability of experimental modulation effects on even t-related potentials (ERPs). The assessment of the within-subject reliabili ty is relevant in all those cases when the subject score is obtained by som e estimation procedure, such as averaging. In these cases, possible deviati ons from the assumptions on which the estimation procedure relies may lead to severely biased results and, consequently, to incorrect functional infer ences. In this study, we applied bootstrap analysis to data from an experim ent aimed at investigating the relationship between ERPs and memory process es. ERPs were recorded from two groups of subjects engaged in a recognition memory task. During the study phase, subjects in Group A were required to make an orthographic judgment on 160 visually presented words, whereas subj ects in Group B were only required to pay attention to the words. During th e test phase all subjects were presented with the 160 previously studied wo rds along with 160 new words and were required to decide whether the curren t word was "old" or "new." To assess the effect of word imagery value, half of the words had a high imagery value and half a low imagery value. Analys es of variance performed on ERPs showed that an imagery-induced modulation of the old/new effect was evident only for subjects who were not engaged in the orthographic task during the study phase. This result supports the hyp othesis that this modulation is due to some aspect of the recognition memor y process and not to the stimulus encoding operations that occur during the recognition memory task. However, bootstrap analysis on the same data show ed that the old/new effect on ERPs was not reliable for all the subjects. T his result suggests that only a cautious inference can be made from these d ata.