Accurately communicating self-relevant and emotional information is a vital function of language, but we have little idea about how these factors impact normal discourse comprehension. In an event-related potential (ERP) study, we fully crossed self-relevance and emotion in a discourse context. Two-sentence social vignettes were presented either in the third or the second person (previous work has shown that this influences the perspective from which mental models are built). ERPs were time-locked to a critical word toward the end of the second sentence which was pleasant, neutral, or unpleasant (e.g., A man knocks on Sandra's/your hotel room door. She/You see(s) that he has a gift/tray/gun in his hand.). We saw modulation of early components (P1, N1, and P2) by self-relevance, suggesting that a self-relevant context can lead to top-down attentional effects during early stages of visual processing. Unpleasant words evoked a larger late positivity than pleasant words, which evoked a larger positivity than neutral words, indicating that, regardless of self-relevance, emotional words are assessed as motivationally significant, triggering additional or deeper processing at post-lexical stages. Finally, self-relevance and emotion interacted on the late positivity: a larger late positivity was evoked by neutral words in self-relevant, but not in non-self-relevant, contexts. This may reflect prolonged attempts to disambiguate the emotional valence of ambiguous stimuli that are relevant to the self. More broadly, our findings suggest that the assessment of emotion and self-relevance are not independent, but rather that they interactively influence one another during word-by-word language comprehension.
ERP studies produce large spatiotemporal data sets. These rich data sets are key to enabling us to understand cognitive and neural processes. However, they also present a massive multiple comparisons problem, potentially leading to a large number of studies with false positive effects (a high Type I error rate). Standard approaches to ERP statistical analysis, which average over time windows and regions of interest, do not always control for Type I error, and their inflexibility can lead to low power to detect true effects. Mass univariate approaches offer an alternative analytic method. However, they have thus far been viewed as appropriate primarily for exploratory statistical analysis and only applicable to simple designs. Here, we present new simulation studies showing that permutation-based mass univariate tests can be employed with complex factorial designs. Most importantly, we show that mass univariate approaches provide slightly greater power than traditional spatiotemporal averaging approaches when strong a priori time windows and spatial regions are used. Moreover, their power decreases only modestly when more exploratory spatiotemporal parameters are used. We argue that mass univariate approaches are preferable to traditional spatiotemporal averaging analysis approaches for many ERP studies.
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