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Using trial-level data and multilevel modeling to investigate within-task change in event-related potentials
Journal article   Peer reviewed

Using trial-level data and multilevel modeling to investigate within-task change in event-related potentials

Hannah I. Volpert-Esmond, Edgar C. Merkle, Meredith P. Levsen, Tiffany A. Ito and Bruce D. Bartholow
Psychophysiology, Vol.55(5), pp.e13044-n/a
05/2018
DOI: 10.1111/psyp.13044
PMCID: PMC5899682
PMID: 29226966

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Abstract

EEG data, and specifically the ERP, provide psychologists with the power to examine quickly occurring cognitive processes at the native temporal resolution at which they occur. Despite the advantages conferred by ERPs to examine processes at different points in time, ERP researchers commonly ignore the trial-to-trial temporal dimension by collapsing across trials of similar types (i.e., the signal averaging approach) because of constraints imposed by repeated measures ANOVA. Here, we present the advantages of using multilevel modeling (MLM) to examine trial-level data to investigate change in neurocognitive processes across the course of an experiment. Two examples are presented to illustrate the usefulness of this technique. The first demonstrates decreasing differentiation in N170 amplitude to faces of different races across the course of a race categorization task. The second demonstrates attenuation of the ERN as participants commit more errors within a task designed to measure implicit racial bias. Although the examples presented here are within the realm of social psychology, the use of MLM to analyze trial-level EEG data has the potential to contribute to a number of different theoretical domains within psychology.
Life Sciences & Biomedicine Neurosciences Neurosciences & Neurology Physiology Psychology Psychology, Biological Psychology, Experimental Science & Technology Social Sciences

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