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Lesion mapping of social problem solving.

Aron K Barbey1, Roberto Colom2, Erick J Paul3

  • 11 Decision Neuroscience Laboratory, University of Illinois, Urbana, IL, USA 2 Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, IL, USA 3 Department of Internal Medicine, University of Illinois, Champaign, IL, USA 4 Department of Psychology, University of Illinois, Champaign, IL, USA 5 Department of Speech and Hearing Science, University of Illinois, Champaign, IL, USA 6 Neuroscience Program, University of Illinois, Champaign, IL, USA 7 Institute for Genomic Biology, University of Illinois, Champaign, IL, USA barbey@illinois.edu jgrafman@ric.org.

Brain : a Journal of Neurology
|July 30, 2014
PubMed
Summary
This summary is machine-generated.

Human intelligence relies on frontal and parietal networks. This study found that working memory, processing speed, and emotional intelligence are key predictors of social problem-solving abilities.

Keywords:
latent variable modellingsocial intelligencevoxel-based lesion–symptom mapping

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Social Cognition

Background:

  • Human intelligence is supported by distributed frontal and parietal networks for goal-directed behavior.
  • The neural basis of social problem-solving remains incompletely understood.

Purpose of the Study:

  • Investigate the neural underpinnings of social problem-solving using a human lesion study.
  • Examine the predictive roles of psychometric intelligence, emotional intelligence, and personality in social problem-solving.

Main Methods:

  • Conducted a lesion study with 144 participants.
  • Administered the Everyday Problem Solving Inventory, Wechsler Adult Intelligence Scale, Mayer-Salovey-Caruso Emotional Intelligence Test, and Neuroticism-Extraversion-Openness Personality Inventory.
  • Utilized voxel-based lesion-symptom mapping and stepwise regression analyses.

Main Results:

  • Working memory, processing speed, and emotional intelligence significantly predicted individual differences in everyday problem-solving.
  • Specific psychological variables selectively predicted performance across different social problem-solving domains.
  • Lesion mapping identified a shared network of frontal, temporal, and parietal regions supporting social problem-solving, psychometric intelligence, and emotional intelligence.

Conclusions:

  • Social problem-solving, psychometric intelligence, and emotional intelligence are supported by an integrated neural network.
  • Findings support an integrative framework for understanding social intelligence.
  • Recommendations are made for applying the Everyday Problem Solving Inventory in health and disease research.