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Related Experiment Video

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Alignability-based free categorization.

John P Clapper1

  • 1Department of Psychology, California State University San Bernardino, United States.

Cognition
|February 26, 2017
PubMed
Summary
This summary is machine-generated.

People perceive objects as similar based on structural alignment, not just shared features. This suggests categorization relies on spatial correspondence for understanding natural kinds.

Keywords:
AlignmentCategorizationCategory constructionConceptsUnsupervised learning

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Area of Science:

  • Cognitive Psychology
  • Perception
  • Categorization

Background:

  • Real-world natural kinds are often based on family resemblance.
  • Laboratory studies show people can be insensitive to family resemblance in categorization tasks.
  • Existing models often define family resemblance by matching dimensions.

Purpose of the Study:

  • To propose and test an alternative conception of family resemblance based on structural alignability.
  • To investigate if structural alignment, independent of shared parts, drives categorization.
  • To determine if abstract spatial correspondence underlies similarity perception.

Main Methods:

  • Five experiments using novel free categorization tasks.
  • Stimuli designed to vary in structural alignment but not necessarily shared parts.
  • Manipulation of object part arrangement to disrupt spatial structure.

Main Results:

  • Structural alignment was sufficient for people to perceive objects as essentially similar.
  • Participants grouped objects into common categories based on abstract alignment.
  • Rearranging object parts eliminated perceived similarity and categorization, even with identical components.

Conclusions:

  • Categorization relies on structural alignment and spatial correspondence, not solely on shared parts or features.
  • Perceptual categories are constructed based on overall similarity defined by alignability.
  • This challenges traditional views of categorization limited by discrete dimensional variation.