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Explanations in the wild.

Justin Sulik1, Jeroen van Paridon2, Gary Lupyan2

  • 1Cognition, Values & Behavior, Ludwig Maximilian University of Munich, Gabelsbergerstrasse 62, Munich 80333, Germany.

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|May 5, 2023
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Summary
This summary is machine-generated.

People find explanations satisfying based on their functional or mechanistic content, not just accuracy. Insight problem-solving ability aids in generating better explanations.

Keywords:
CreativityExplanationsInsightMetacognitionPerspective-taking

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Area of Science:

  • Cognitive Science
  • Psychology
  • Philosophy of Science

Background:

  • Explanations vary in their perceived quality, even when factually accurate.
  • Understanding the factors influencing explanation satisfaction is crucial for effective communication and learning.

Purpose of the Study:

  • To identify features associated with high-quality explanations.
  • To assess individuals' ability to judge explanation quality.
  • To determine cognitive traits linked to generating satisfying explanations.

Main Methods:

  • Laypeople generated and rated thousands of open-ended explanations for 'Why?' questions.
  • Analysis focused on explanation properties, self-assessment accuracy, and cognitive correlates.
  • Insight problem-solving ability was measured.

Main Results:

  • Explanation satisfaction is best predicted by functional or mechanistic content.
  • People are better at judging explanation accuracy than their satisfying nature.
  • Insight problem-solving ability strongly correlates with generating satisfying explanations.

Conclusions:

  • A pluralistic view of explanation, incorporating both functional and mechanistic aspects, best explains satisfaction.
  • Self-assessment of explanation quality is limited, particularly regarding subjective satisfaction.
  • Cognitive abilities, specifically insight problem-solving, play a key role in generating effective explanations.