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A deep convolutional neural network trained for lightness constancy is susceptible to lightness illusions.
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Updated: Feb 11, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
Lightness matching and perceptual similarity.
Khushbu Y Patel1, Anudhi P Munasinghe1,2, Richard F Murray1
1Department of Psychology and Centre for Vision Research, York University, Toronto, Canada.
This study shows people can accurately match surface reflectance by adjusting lighting. Findings support the idea that our brains explicitly estimate reflectance, challenging alternative theories.
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Area of Science:
- Visual Perception
- Color Science
- Psychophysics
Background:
- Lightness constancy, perceiving surface reflectance under varying illumination, is traditionally explained by discounting lighting intensity.
- An alternative theory suggests lightness constancy relies on a perceptual similarity metric, not explicit reflectance estimation.
Purpose of the Study:
- To compare the traditional discounting-of-lighting theory with an alternative similarity-based theory of lightness constancy.
- To investigate whether observers can make explicit reflectance estimates in a novel task.
Main Methods:
- A free-adjustment reflectance-matching task was developed, allowing observers to adjust both illuminance and reflectance of a match stimulus.
- The task was performed using both physical objects and computer-rendered images to assess stimulus modality effects.
Main Results:
- Observers successfully matched reflectance in the free-adjustment task, supporting the explicit reflectance estimation view.
- Lightness constancy performance was comparable for physical objects and rendered images, though rendered stimuli showed a potential glow artifact.
Conclusions:
- The results provide evidence for explicit reflectance estimation in lightness constancy, challenging purely similarity-based accounts.
- The study validates a new method for studying lightness constancy and highlights potential differences between physical and virtual stimuli.

