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Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

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Related Experiment Video

Updated: May 12, 2026

A Semantic Priming Event-related Potential (ERP) Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder
08:17

A Semantic Priming Event-related Potential (ERP) Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

Published on: April 12, 2018

Detecting semantic priming at the single-trial level.

Jeroen Geuze1, Marcel A J van Gerven, Jason Farquhar

  • 1Radboud University Nijmegen, Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, The Netherlands. j.geuze@donders.ru.nl

Plos One
|April 9, 2013
PubMed
Summary
This summary is machine-generated.

This study detects semantic priming in single brain activity trials using machine learning. This approach could advance Brain Computer Interface (BCI) development by analyzing EEG data for word relatedness.

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Last Updated: May 12, 2026

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Published on: May 9, 2019

Area of Science:

  • Cognitive Neuroscience
  • Machine Learning
  • Brain-Computer Interfaces

Background:

  • Semantic priming research traditionally relies on analyzing electroencephalography (EEG) over numerous trials and subjects.
  • Detecting semantic priming at the single-trial level presents a significant challenge in cognitive neuroscience.

Purpose of the Study:

  • To investigate the feasibility of detecting semantic priming at the single-trial level using machine learning.
  • To explore the potential application of single-trial semantic priming detection in developing Brain Computer Interfaces (BCIs).

Main Methods:

  • An experiment was conducted where participants judged the relatedness of word pairs.
  • A machine learning classifier was trained using one second of electroencephalography (EEG) data to distinguish between related and unrelated word judgments.
  • Classification accuracy was assessed both within individual subjects and across all subjects.

Main Results:

  • Per-subject classifier accuracy ranged from 54% to 67%, significantly above chance for all 12 participants.
  • Cross-subject classifier accuracy ranged from 51% to 63%, significantly above chance for 11 out of 12 subjects.
  • These results indicate a generalizable effect of semantic priming detectable at the single-trial level.

Conclusions:

  • Machine learning techniques can effectively detect semantic priming at the single-trial level from EEG data.
  • Single-trial semantic priming detection shows promise for advancing Brain Computer Interface (BCI) technologies.
  • The findings suggest that brain activity patterns related to semantic processing are discernible even in short EEG epochs.