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Related Experiment Video

Updated: Jun 27, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

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Published on: November 2, 2012

Explanation and categorization: how "why?" informs "what?".

Tania Lombrozo1

  • 1Department of Psychology, UC Berkeley, 3210 Tolman Hall, Berkeley, CA 94720, USA. lombrozo@berkeley.edu

Cognition
|December 20, 2008
PubMed
Summary
This summary is machine-generated.

Explanations shape how we categorize objects by influencing feature importance. Functional explanations, compared to mechanistic ones, alter which features are considered most relevant for categorization decisions.

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Area of Science:

  • Cognitive Science
  • Psychology
  • Artificial Intelligence

Background:

  • Theoretical and empirical research indicates a strong link between explanation and categorization.
  • Conceptual representations are structured by explanations, influencing feature salience in decision-making.

Purpose of the Study:

  • To investigate how different types of explanations (mechanistic vs. functional) affect feature importance in categorization.
  • To test the hypothesis that explanations structure conceptual representations and guide categorization.

Main Methods:

  • Two experiments were conducted.
  • Participants' categorization judgments were analyzed after features were explained mechanistically (proximate causes) or functionally (goals/purposes).

Main Results:

  • Explanation type significantly impacted the relative importance of features during categorization.
  • Functional explanations reversed previously observed effects of 'causal status' on feature importance.

Conclusions:

  • Explanatory importance of features influences categorization decisions.
  • Explanatory relationships, alongside causal ones, are crucial for understanding conceptual representation and categorization.