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Related Experiment Video

Updated: Jun 1, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

On the filter approach to perceptual transparency.

Franz Faul¹, Vebjørn Ekroll

¹Institut für Psychologie, Universität Kiel, Germany. ffaul@psychologie.uni-kiel.de

Journal of Vision

|June 11, 2011

Summary

This study refines a perceptual transparency model for optical filters. It establishes robust parameter estimation and links physical properties to perceptual dimensions like hue, saturation, transmittance, and clarity.

Area of Science:

Visual Perception
Color Science
Optical Physics

Background:

A prior filter model accurately predicted perceived transparency.
Robust parameter estimation and perceptual relevance were lacking.

Purpose of the Study:

Elaborate on the filter model of perceptual transparency.
Develop robust parameter estimation methods.
Relate physical filter properties to perceptual dimensions.

Main Methods:

Robust estimation of model parameters.
Transformation of physical parameters to perceptual dimensions (hue, saturation, transmittance, clarity).
Experimental determination of iso-saturation curves.

Main Results:

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Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

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Last Updated: Jun 1, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Physical parameters were transformed into perceptual dimensions (H, S, V, C).
Clarity (C) correlates with refractive index; transmittance (V) and saturation (S) correlate with thickness.
Relative filter thickness can be estimated from S and V.

Conclusions:

The enhanced model provides a robust link between physical filter properties and perceived transparency.
Perceptual dimensions offer a more intuitive understanding of filter characteristics.
The model aids in predicting and controlling perceived transparency based on physical parameters.