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Related Experiment Videos

Coding spatial variations in faces and simple shapes: a test of two models

G Rhodes1, S Carey, G Byatt

  • 1University of Canterbury, Christchurch, New Zealand.

Vision Research
|November 3, 1998
PubMed
Summary
This summary is machine-generated.

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This study found that recognizing faces and shapes relies on absolute coding, not norm-based coding. Caricatures are memorable due to their distinctiveness, supporting the absolute coding model for visual perception.

Area of Science:

  • Cognitive psychology
  • Computational neuroscience
  • Visual perception

Background:

  • Facial recognition relies on variations within a shared configuration of basic elements.
  • Two models explain this: norm-based coding (deviation from an average face) and absolute coding (absolute values in a coordinate system).
  • These models predict different factors influencing image recognizability.

Purpose of the Study:

  • To investigate whether norm-based or absolute coding better explains face and shape recognition.
  • To determine the impact of distinctiveness, distortion, norm deviation, and norm-deviation displacement on recognizability.
  • To test these models across famous faces, newly learned faces, and simple shapes.

Main Methods:

  • Experiment 1: Tested recognition of famous faces with distortions (caricatures, anticaricatures, lateral distortions).

Related Experiment Videos

  • Experiment 2: Tested recognition of newly learned faces and simple shapes with similar distortions.
  • Measured the influence of four variables: distinctiveness, distortion, norm distance, and norm-deviation displacement.

    • Main Results:

    • Results favored the absolute coding model for both faces and shapes.
    • Image recognizability was primarily influenced by distinctiveness and distortion from the veridical target.
    • Caricatures' impact stems from their high distinctiveness, aligning with absolute coding predictions.

    Conclusions:

    • Absolute coding provides a more effective framework for understanding the recognition of faces and simple shapes.
    • The distinctiveness of an image, as predicted by absolute coding, is a key factor in its recognizability.
    • This challenges the primacy of norm-based coding in explaining visual recognition of configurations.