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Functional localization and double dissociations: the relationship between internal structure and behavior.

David A Medler1, Michael R W Dawson, Alan Kingstone

  • 1Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, United States. dmedler@mcw.edu

Brain and Cognition
|February 15, 2005
PubMed
Summary
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Computational models help interpret lesioning studies in cognitive neuroscience. Lesion effects depend on the functional localization and internal structure of brain networks, cautioning against direct inference.

Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Neuroscience

Background:

  • Lesioning studies are crucial for understanding cognitive architecture.
  • Interpreting lesion data often relies on implicit assumptions like modularity and locality.
  • Computational models offer a framework to test these assumptions.

Purpose of the Study:

  • To investigate the functional localization assumption in interpreting lesioning data, particularly double dissociations.
  • To explore the relationship between a network's internal structure and behavioral deficits after lesioning.

Main Methods:

  • Trained computational networks on abstract and concrete problems.
  • Systematically lesioned the trained networks.
  • Analyzed both network behavior and internal structure to correlate deficits with ablated areas.

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Main Results:

  • Performance deficits directly correlated with the magnitude and location of ablated internal network structures.
  • Specific behavioral dissociations were observed when lesioned areas exhibited high functional localization.
  • Ablation of areas with little functional localization resulted in minimal behavioral dissociations.

Conclusions:

  • The degree of functional localization within a network dictates the observed behavioral dissociations following lesioning.
  • A direct one-to-one mapping between internal structure and behavioral dissociations is not guaranteed.
  • Cognitive neuroscientists should exercise caution when inferring functional architecture from lesioning data.