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The neural code of intelligence: From correlation to causation.

Norbert Jaušovec1

  • 1Ruperče 46, 2229 Malečnik, Slovenia.

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Summary

This study explores the brain basis of general intelligence (g), finding that brain structure, white matter integrity, and network flexibility are key. Efficient information transfer and flexible network transitions underpin higher cognitive abilities.

Keywords:
Brain structureIntelligenceMetastabilityMulti-demand systemNetwork neuroscienceNeural efficiencyP-FIT

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Intelligence Research

Background:

  • Traditional research on intelligence (g) focuses on psychological constructs.
  • This study adopts a 'brain perspective,' examining neural underpinnings.
  • Methodologies include correlational and interventional studies for causal insights.

Purpose of the Study:

  • To investigate the neuroanatomical and functional correlates of general intelligence (g).
  • To explore the role of brain structure, white matter integrity, and network dynamics.
  • To synthesize findings from various theoretical frameworks, including Network Neuroscience Theory.

Main Methods:

  • Correlational analyses examining brain volume, surface, thickness, and white matter integrity (using diffusion tensor imaging).
  • Functional neuroimaging studies evaluating theories like neural efficiency, P-FIT, and the multi-demand (MD) system.
  • Interventional studies using non-invasive brain stimulation and behavioral training.

Main Results:

  • Brain volume shows a modest positive correlation with g (9-16% variance), linked to greater computational power.
  • White matter integrity positively correlates with intelligence, emphasizing efficient inter-hemispheric and inter-areal information transfer.
  • Network flexibility and dynamics are proposed as foundational for general intelligence.

Conclusions:

  • General intelligence (g) is predominantly linked to lateral prefrontal attentional control over posterior sensory processing.
  • The capacity for flexible transitions between neural network states is central to intelligence.
  • Intervention studies tentatively support correlational findings, suggesting plasticity in neural networks related to intelligence.