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Towards Semantic Brain Mapping Methodology Based on a Multidimensional Markup of Continuous Russian-Language Texts:

B M Velichkovsky1, V I Zabotkina2, Z A Nosovets3

  • 1Professor, Corresponding Member of the Russian Academy of Sciences, Member of Academia Europaea (MAE), Chief Researcher, National Research Center "Kurchatov Institute", 1 Akademika Kurchatova Square, Moscow, 123182, Russia, , Russian State University for the Humanities, 6 Miusskaya Square, Moscow, 125993, Russia; Senior Professor, Technische Universität Dresden, Zellescher Weg 17, Room A221, Dresden, 01069, Germany.

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Summary

This study maps semantic categories in the brain using functional MRI and linguistic analysis. Findings reveal widely distributed brain clusters, including subcortical areas, involved in understanding meaning, suggesting a shift from thematic contrasts to multidimensional semantic modeling.

Keywords:
BOLD signalcognitive subtractionecological validityhaemodynamic response functionhemispheric lateralizationmodeling in semanticsprincipal components analysissubcortical structures

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Area of Science:

  • Neuroscience
  • Linguistics
  • Cognitive Science

Background:

  • Understanding how the brain processes meaning is crucial for cognitive science.
  • Previous research often used thematic contrasts, which may be insufficient for text comprehension.

Purpose of the Study:

  • To investigate the neural representation of semantic categories in the human brain.
  • To compare brain activation across different thematic domains (nature, technology, self-identity).
  • To propose a new model for understanding text comprehension.

Main Methods:

  • Functional MRI (fMRI) to measure brain activity (BOLD signal).
  • Linguistic annotation of oral Russian texts.
  • Analysis of cortical activation during listening to varied thematic content.

Main Results:

  • Thematic domain contrasts showed minimal significant differences in brain activation.
  • Fifteen distinct clusters, widely distributed across the brain, were identified for semantic categories.
  • Deep subcortical structures and right hemisphere lateralization were implicated in processing specific semantic categories.

Conclusions:

  • Thematic group contrasts are insufficient for studying text comprehension mechanisms.
  • Multidimensional modeling of semantic categories over time is a more effective approach.
  • Text understanding involves widespread neural networks, including subcortical regions and right-hemisphere dominance for meaning.