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Related Concept Videos

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Related Experiment Video

Updated: Apr 3, 2026

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Neural Representations Across Multiple Stages in a Go/No-Go Task: Evidence From Multivariate Pattern Analysis.

Shangfeng Han1, Junlong Huang2, Yuejia Luo3,4,5,6,7

  • 1Department of Psychology and Center for Brain and Cognitive Sciences; Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou, China.

Psychophysiology
|April 2, 2026
PubMed
Summary
This summary is machine-generated.

This study reveals that the brain processes Go/No-Go tasks through multiple stages, using multivariate pattern analysis (MVPA) to uncover dynamic neural codes influenced by trial history.

Keywords:
go/no‐gomultivariate pattern analysisneural representation

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Area of Science:

  • Cognitive Neuroscience
  • Computational Psychiatry

Background:

  • The Go/No-Go task is a standard paradigm in psychological research.
  • Traditional analysis of event-related potentials (ERPs) in this task offers limited insight into temporal dynamics.

Purpose of the Study:

  • To investigate the multi-stage temporal dynamics of neural representations during the Go/No-Go task.
  • To compare univariate ERP analysis with multivariate pattern analysis (MVPA) for deeper insights.

Main Methods:

  • Electroencephalography (EEG) data from 72 participants performing a Go/No-Go task.
  • Univariate ERP analysis and MVPA techniques including decoding, temporal generalization, and weight projection.

Main Results:

  • Go and No-Go trials were distinguishable by ~100 ms using MVPA.
  • Distinct temporal phases of neural representation were identified: early (~300-400 ms), specific (~400-700 ms), and late (~700-1700 ms).
  • Neural activity involved occipital, prefrontal, and parietal regions, with trial history influencing early and late processing stages.

Conclusions:

  • The Go/No-Go task relies on a dynamic, multi-stage neural code.
  • Neural representations evolve over time and are modulated by recent trial history.
  • MVPA offers a more detailed understanding of temporal dynamics than traditional ERP analysis.