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Related Concept Videos

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: Jun 3, 2026

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

Functional network analysis reveals differences in the semantic priming task.

Stefan Schinkel1, Gorka Zamora-López, Olaf Dimigen

  • 1Department of Physics, Humboldt University at Berlin, Germany. schinkel@physik.hu-berlin.de

Journal of Neuroscience Methods
|March 8, 2011
PubMed
Summary
This summary is machine-generated.

Graph theory analysis reveals task-dependent brain network differences during cognitive tasks. This approach offers new insights into functional connectivity and cognitive processes like language perception.

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Area of Science:

  • Neuroscience
  • Graph Theory
  • Cognitive Science

Background:

  • Graph theoretical analysis of brain networks is emerging.
  • Research has focused on resting-state networks and clinical applications.
  • Functional resting-state networks exhibit small-world properties that change with pathology or aging.

Purpose of the Study:

  • To apply graph theoretical tools to functional connectivity during high-level cognitive processing.
  • To investigate task-dependent differences in cognitive processes like language perception.
  • To demonstrate the feasibility of graph theory in cognitive neuroscience.

Main Methods:

  • Utilizing graph theoretical analysis on neurophysiological measurements.
  • Investigating functional brain networks during cognitive tasks in healthy adults.
  • Employing methods analogous to event-related potentials (ERPs) in psychological research.

Main Results:

  • Graph theoretical approaches can identify systematic, task-dependent differences in functional brain networks.
  • The findings align with established neuroimaging study results.
  • This method is feasible for analyzing high-level cognitive functions.

Conclusions:

  • Graph theory provides a valuable framework for understanding functional brain connectivity during cognitive tasks.
  • Task-dependent network alterations are detectable using graph theoretical analysis.
  • This approach complements existing neuroimaging techniques for cognitive neuroscience research.