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Meta-Intelligence: Understanding, Control, and Interactivity between Creative, Analytical, Practical, and

Robert J Sternberg1, Vlad Glaveanu2,3, Sareh Karami4

  • 1Department of Human Development, Cornell University, Ithaca, NY 14853, USA.

Journal of Intelligence
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

Understanding problem-solving requires examining how people frame issues and find solutions. Meta-intelligence, encompassing understanding, control, and coordination, explains variability in solution quality by considering individual, contextual, and interactive constraints.

Keywords:
WICSanalytical skills and attitudesconstraintcreative skills and attitudesintellectual skills and attitudesmeta-intelligencepractical skills and attitudesproblem-solvingsolutionssystemswisdom-based skills and attitudes

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Area of Science:

  • Cognitive Psychology
  • Problem-Solving Research
  • Intelligence Studies

Background:

  • Variability in problem-solving solution quality necessitates a deeper understanding of problem framing and solution-finding processes.
  • Sternberg's WICS model provides a conceptual framework for analyzing different approaches to problem-solving.

Purpose of the Study:

  • To explore the relationship between creative, analytical, practical, and wisdom-based approaches in problem-solving.
  • To introduce and define the construct of meta-intelligence for understanding problem-solving dynamics.
  • To propose a model explaining how constraints at individual, contextual, and interactive levels influence problem framing and solution quality.

Main Methods:

  • Conceptual analysis based on Sternberg's WICS model of problem-solving.
  • Development of the meta-intelligence construct to integrate different problem-solving approaches.
  • Examination of constraints (individual, contextual, interactive) influencing problem framing and solution generation.

Main Results:

  • Meta-intelligence integrates understanding, control, and coordination of creative, analytical, practical, and wisdom-based problem-solving approaches.
  • Constraints can operate at individual (metacomponents), contextual (situational preferences), and interactive levels.
  • The meta-intelligence model effectively explains variations in how individuals frame problems and the resulting quality of solutions.

Conclusions:

  • A deeper understanding of problem framing and solution-finding processes, guided by meta-intelligence, is crucial for explaining solution quality variability.
  • The proposed model of meta-intelligence provides a framework for analyzing how individuals and contexts interact to produce diverse problem-solving outcomes.
  • This model helps elucidate why some solutions are more comprehensive and effective than others.