Using N2pc variability to probe functionality: Linear mixed modelling of trial EEG and behaviour

Abstract

This paper has two concurrent goals. On one hand, we hope it will serve as a simple primer in the use of linear mixed modelling (LMM) for inferential statistical analysis of multimodal data. We describe how LMM can be easily adopted for the identification of trial-wise relationships between disparate measures and provide a brief cookbook for assessing the suitability of LMM in your analyses. On the other hand, this paper is an empirical report, probing how trial-wise variance in the N2pc, and specifically its sub-component the NT, can be predicted by manual reaction time (RT) and stimuli parameters. Extant work has identified a link between N2pc and RT that has been interpreted as evidence of a direct and causative relationship. However, results have left open the less-interesting possibility that the measures covary as a function of motivation or arousal. Using LMM, we demonstrate that the relationship only emerges when the NT is elicited by targets, not distractors, suggesting a discrete and functional relationship. In other analyses, we find that the target-elicited NT is sensitive to variance in distractor identity even when the distractor cannot itself elicit consistently lateralized brain activity. The NT thus appears closely linked to attentional target processing, supporting the propagation of target-related information to response preparation and execution. At the same time, we find that this component is sensitive to distractor interference, which leaves open the possibility that NT reflects brain activity responsible for the suppression of irrelevant distractor information.