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High perceptual load is thought to impair the early processing of task-irrelevant distractors. In contrast, for emotional faces, previous studies have shown that early event-related potentials (ERPs), the P1, the N170, and, albeit to a lesser degree, the EPN, are relatively resistant to perceptual load manipulations. However, the temporal dynamics of the interaction between load and processing of emotional distractor faces have been neglected so far. In this preregistered EEG study (N = 40), we investigated effects of perceptual load and different interstimulus intervals (ISIs) on ERPs to fearful and neutral task-irrelevant faces. We used a task with identical visual input regardless of perceptual load (high vs. low), and four ISIs between task and face onset (100 ms, 300 ms, 600 ms, 900 ms). Results show that emotional ERP modulations depend on load manipulations as well as on specific ISIs between the perceptual task and face onset. Emotional P1 effects were modulated by load, irrespective of the ISI, while emotional N170 and EPN effects were independent of load, but modulated by the ISI. In particular, emotion effects for the EPN were only observed after a prolonged period between load task and face onset (ISI900), suggesting a strong vulnerability of this component to any competing task. Taken together, our findings show that early ERP components for fearful expressions show dissociable responses to load and timing manipulations.
Pubmed ID: 32553724
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Software for source analysis and dipole localization in EEG and MEG research. BESA Research has been developed on the basis of 20 years experience in human brain research by Michael Scherg, University of Heidelberg, and Patrick Berg, University of Konstanz. BESA Research is a highly versatile and user-friendly Windows program with optimized tools and scripts to preprocess raw or averaged data for source analysis. All important aspects of source analysis are displayed in one window for immediate selection of a wide range of tools. BESA Research provides a variety of source analysis algorithms, a standardized realistic head model (FEM), and allows for fast and easy hypothesis testing and integration with MRI and fMRI.
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