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

Clayton Hickey1, Damiano Grignolio1, Vinura Munasinghe1

  • 1Center for Human Brain Health and School of Psychology, University of Birmingham, UK.

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Summary

Linear mixed modelling (LMM) helps analyze multimodal data. This study uses LMM to show the NT component

Keywords:
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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Psychology

Background:

  • Previous research suggests a link between N2pc and reaction time (RT).
  • This link is often interpreted as a direct relationship between visual attention and response.
  • Alternative explanations, such as motivation or arousal, have not been fully excluded.

Purpose of the Study:

  • To introduce linear mixed modelling (LMM) for multimodal data analysis.
  • To investigate trial-wise variance in the N2pc, specifically its NT sub-component.
  • To determine if manual reaction time (RT) and stimulus parameters predict NT variance.

Main Methods:

  • Application of linear mixed modelling (LMM) for inferential statistical analysis.
  • Analysis of multimodal data including N2pc, NT, manual reaction time (RT), and stimulus parameters.
  • Assessment of LMM suitability for analyzing relationships between disparate measures.

Main Results:

  • The relationship between N2pc and RT was confirmed specifically for the NT component elicited by targets, not distractors.
  • Target-elicited NT variance was sensitive to distractor identity, even when distractors did not elicit lateralized brain activity.
  • NT appears linked to attentional target processing and distractor suppression.

Conclusions:

  • LMM is a viable tool for analyzing multimodal data and identifying trial-wise relationships.
  • The NT component plays a role in attentional target processing and response preparation.
  • NT is also involved in suppressing irrelevant distractor information, supporting its functional role in attention.