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Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
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Related Experiment Video

Updated: Oct 25, 2025

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Average faces: How does the averaging process change faces physically and perceptually?

Isabelle Bülthoff1, Mintao Zhao2

  • 1Max Planck Institute for Biological Cybernetics, Germany.

Cognition
|August 7, 2021
PubMed
Summary
This summary is machine-generated.

Creating average faces for research makes them harder to tell apart as more faces are averaged. This impacts face recognition performance, dropping from high accuracy to chance levels as averaging increases.

Keywords:
Average faceEye movementsFace normFace recognitionFace similarity

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Area of Science:

  • Cognitive Psychology
  • Computer Vision
  • Neuroscience

Background:

  • Average faces are crucial in face recognition research for theoretical concepts and attribute manipulation.
  • The impact of the face averaging process on face differentiation remains unclear.

Purpose of the Study:

  • Investigate how increasing the number of faces in averaging affects computational similarity and discrimination performance.
  • Analyze the relationship between face similarity and discrimination accuracy.
  • Examine eye movement patterns during face comparison tasks with varying averaging levels.

Main Methods:

  • Employed 3D-face averaging techniques.
  • Utilized eye-movement tracking to monitor participant attention.
  • Computed image-based face similarity metrics.
  • Participants performed same-different judgments on average faces with systematically increased averaging levels.

Main Results:

  • Computational similarity between average faces nonlinearly increased with more averaging.
  • Face discrimination performance significantly decreased, dropping to chance level with 80 averaged faces.
  • A nonlinear, exponential relationship was observed between face similarity and discrimination performance.
  • Increased task difficulty led to more fixations, but fixation distribution across facial features remained consistent.

Conclusions:

  • Increasing face averaging nonlinearly increases computational similarity and decreases discrimination performance.
  • The findings provide insights into the theoretical role of average faces and practical guidance for manipulating face identity in research.