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Decomposing intuitive components in a conceptual problem solving task.

Rolf Reber1, Marie-Antoinette Ruch-Monachon, Walter J Perrig

  • 1Department of Psychology, University of Bergen, Christiesgate 12, Bergen, Norway. rolf.reber@psysp.uib.no

Consciousness and Cognition
|July 4, 2006
PubMed
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Objective problem-solving accuracy surpasses subjective feelings of closeness. Differentiating task types and components like processing style reveals nuanced links between intelligence, personality, and intuitive problem-solving effectiveness.

Area of Science:

  • Cognitive Psychology
  • Psychology of Problem Solving

Background:

  • Intuitive problem solving often involves a subjective sense of 'closeness' to a solution.
  • Previous research indicates objective hypothesis accuracy exceeds subjective closeness ratings.

Purpose of the Study:

  • To replicate and refine findings on objective vs. subjective closeness in intuitive problem solving.
  • To investigate the relationships between problem-solving components (performance, processing style, implicit knowledge, subjective closeness) with intelligence and personality traits.

Main Methods:

  • Separated conceptual intuitive problem solving from rational incremental and sudden insight tasks.
  • Employed precise measures in a conceptual problem-solving task.
  • Examined correlations between problem-solving components, verbal intelligence, and personality measures (faith in intuition, openness, conscientiousness).

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Main Results:

  • Objective hypothesis closeness was confirmed to be a better predictor of accurate solutions than subjective ratings.
  • Verbal intelligence correlated with problem-solving performance but not processing style or implicit knowledge.
  • Faith in intuition, openness, and conscientiousness correlated with processing style, not implicit knowledge.

Conclusions:

  • Decomposing intuitive problem-solving into distinct components is crucial.
  • Understanding the interplay between processing style, implicit knowledge, intelligence, and personality is key for predicting problem-solving outcomes.