Progress in the study of emotion and emotion regulation has increasingly been informed by neuroscientific methods. This article focuses on two components of the event-related potential (ERP)—the P300 and the late positive potential (LPP)—and how they can be used to understand the interaction between the more automatic and controlled processing of emotional stimuli. Research is reviewed exploring: the dynamics of emotional response as indexed at early and late latencies; neurobiological correlates of emotional response; individual and developmental differences; ways in which the LPP can be utilized as a measure of emotion regulation. Future directions for the application of ERP/electroencephalogram (EEG) in achieving a more complete understanding of emotional processing and its regulation are presented.

Emotional stimuli have been shown to elicit increased perceptual processing and attentional allocation. The late positive potential (LPP) is a sustained P300-like component of the event-related potential that is enhanced after the presentation of pleasant and unpleasant pictures as compared with neutral pictures. In this study, the LPP was measured using dense array electroencephalograph both before and after pleasant, neutral, and unpleasant images to examine the time course of attentional allocation toward emotional stimuli. Results from 17 participants confirmed that the LPP was larger after emotional than neutral images and that this effect persisted for 800 ms after pleasant picture offset and at least 1,000 ms after unpleasant picture offset. The persistence of increased attention after unpleasant compared to pleasant stimuli is consistent with the existence of a negativity bias. Overall, these results indicate that attentional capture of emotion continues well beyond picture presentation and that this can be measured with the LPP. Implications and directions for future research are discussed.

Schizophrenia is characterized as a neurodevelopmental disorder¹ of distributed brain dysconnectivity,² emerging from complex biological alterations that affect large-scale neural systems.³ Its symptoms are correspondingly pervasive,⁴ leading to lifelong disability for most patients⁵ and profound economic consequences. Understanding neural disturbances in schizophrenia constitutes a critical research goal that necessitates identification of pathophysiologic mechanisms and biomarkers that aid risk prediction. Growing sophistication in noninvasive neuroimaging offers a way to characterize large-scale neural system disturbances in psychiatric illness^6-8 by studying low-frequency fluctuations in the blood oxygenation level–dependent (BOLD) signal at rest⁷ (ie, via resting-state functional connectivity magnetic resonance imaging [rs-fcMRI]). This technique is increasingly applied to the study of neuropsychiatric conditions given its brief acquisition time, cost-effectiveness, and reproducibility^6,9 based on the hypothesis that conditions such as schizophrenia are brain disorders that affect exchange of information across large-scale neural networks.