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Nathan Destler1, Manish Singh1, Jacob Feldman1

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Researchers explored how humans perceive unfamiliar shapes by measuring discrimination thresholds along shape continua. Findings reveal systematic variations in sensitivity, suggesting underlying categorical structures in mental shape space.

Keywords:
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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Psychology

Background:

  • Human shape perception involves complex mental representations.
  • Categorical perception, where category boundaries influence perception, is known for familiar shapes.
  • The representation of unfamiliar shapes remains less understood.

Purpose of the Study:

  • To investigate the structure of mental shape space for unfamiliar shapes.
  • To identify dimensions that influence shape discrimination.
  • To test for categorical perception effects in novel shape continua.

Main Methods:

  • Generated shape continua ('morph-spaces') by interpolating between pairs of shapes.
  • Measured shape discrimination thresholds using an adaptive procedure along these continua.
  • Targeted specific categorical distinctions (e.g., part number, symmetry).

Main Results:

  • Discrimination thresholds varied systematically along the morph-spaces, deviating from uniformity.
  • Sensitivity was not uniform, indicating specific regions of enhanced or reduced discriminability.
  • A Bayesian skeletal shape representation model accounted for observed sensitivity variations.

Conclusions:

  • Mental shape space for unfamiliar objects exhibits systematic structure and categorical sensitivity.
  • Perception of novel shapes is influenced by underlying representational dimensions.
  • Bayesian skeletal models offer a promising approach to understanding shape similarity and perception.