ArticleVision research2012
When is spatial filtering enough? Investigation of brightness and lightness perception in stimuli containing a visible illumination component.
Article in Vision research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A deep convolutional neural network trained for lightness constancy is susceptible to lightness illusions.Journal of vision · 2026Article
- Relationships among lightness illusions uncovered by analyses of individual differences.Journal of vision · 2025Article
- Dogs (Canis lupus familiaris) are susceptible to the Kanizsa's triangle illusion.Animal cognition · 2022Article
- Isolation of brightness induction effects on target patches from adjacent surrounds and remote backgrounds.Frontiers in human neuroscience · 2022Article
- Simple Assumptions to Improve Markov Illuminance and Reflectance.Frontiers in psychology · 2022Article
- A model of lightness perception guided by probabilistic assumptions about lighting and reflectance.Journal of vision · 2020Article
- Dissecting the influence of the collinear and flanking bars in White's effect.Vision research · 2016Article
- Article
- What visual illusions tell us about underlying neural mechanisms and observer strategies for tackling the inverse problem of achromatic perception.Frontiers in human neuroscience · 2015Article
- Scale-invariance in brightness illusions implicates object-level visual processing.Scientific reports · 2014Article
- A unified account of perceptual layering and surface appearance in terms of gamut relativity.PloS one · 2014Article
- A temporal window for estimating surface brightness in the Craik-O'Brien-Cornsweet effect.Frontiers in human neuroscience · 2014Article
- Lighting direction and visual field modulate perceived intensity of illumination.Frontiers in psychology · 2013Article
- A neurodynamical model of brightness induction in v1.PloS one · 2013Article
- The Influence of Physical Illumination on Lightness Perception in Simultaneous Contrast Displays.i-PerceptionArticle
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Authors and funding
2 authors.
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Abstract
Brightness (perceived intensity) and lightness (perceived reflectance) matching were investigated in seven well-known visual stimuli that contain a visible shadow or transparent overlay. These stimuli are frequently upheld as evidence that low-level spatial filtering is inadequate to explain brightness/lightness illusions and that additional mid- or high-level mechanisms are required. The argument in favor of rejecting low-level spatial filtering explanations has been founded on the erroneous assumption that equating test patch and near surround luminance is sufficient to control for and rule out this type of mechanism. We tested this idea by comparing the matching behavior of four observers to the predictions of the ODOG multiscale filtering model (Blakeslee & McCourt, 1999). Lightness and brightness matching differed significantly only when test patches appeared in shadow or beneath a transparency. Lightness and brightness matches were both significantly larger under these conditions; however, the lightness matches greatly exceeded the brightness matches. Lightness matches were greater for test patches in shadow or beneath a transparency because lightness matches under these conditions were based on conscious inferential (not sensory-level) judgments where observers attempted to discount the difference in illumination. The ODOG model accounted for approximately 80% of the total variance in the brightness matches (as well as in the lightness matches for targets not in shadow or beneath a transparency), and successfully predicted the relative magnitude of these matches in five of the seven stimulus sets. These results indicate that multiscale spatial filtering provides a unified and parsimonious explanation for brightness perception in these stimuli and imply that higher-level mechanisms are not required to explain them. The model was not as successful for the argyle and wall of blocks illusions in that it incorrectly rank-ordered the relative magnitude of the effects across different versions of the stimuli. It is an important question whether such model failures are due to known but corrigible limitations of the ODOG model or whether they will require other (possibly higher-level) explanations.
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