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

Area of Science:

• Cognitive Neuroscience
• Psychology
• Psychophysiology

Background:

• Electroencephalography (EEG) and event-related potentials (ERPs) offer high temporal resolution for studying cognitive processes.
• Traditional ERP analysis often uses signal averaging, neglecting trial-to-trial variability and temporal dynamics due to ANOVA constraints.
• The trial-to-trial temporal dimension is crucial for understanding the evolution of neurocognitive processes.

Purpose of the Study:

• To introduce and demonstrate the advantages of multilevel modeling (MLM) for analyzing trial-level ERP data.
• To investigate changes in neurocognitive processes across the course of an experiment using MLM.
• To highlight MLM's potential beyond social psychology in various psychological domains.

Main Methods:

• Utilized multilevel modeling (MLM) to analyze trial-level electroencephalography (EEG) data, specifically event-related potentials (ERPs).
• Applied MLM to examine neurocognitive process changes over experimental time.
• Presented two empirical examples illustrating MLM's application in social psychology research.

Main Results:

• Demonstrated decreasing differentiation in N170 amplitude to faces of different races during a race categorization task.
• Showed attenuation of the error-related negativity (ERN) with increased errors in an implicit racial bias task.
• Confirmed MLM's capability to detect subtle, time-dependent neurocognitive effects.

Conclusions:

• Multilevel modeling (MLM) provides a powerful alternative to signal averaging for ERP analysis, capturing trial-level dynamics.
• MLM enables the investigation of neurocognitive changes across experimental trials, offering richer insights into cognitive functions.
• The application of MLM to trial-level EEG data has broad implications for diverse research areas within psychology.