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An exponential filter model predicts lightness illusions

Overview of attention for article published in Frontiers in Human Neuroscience, June 2015
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Title
An exponential filter model predicts lightness illusions
Published in
Frontiers in Human Neuroscience, June 2015
DOI 10.3389/fnhum.2015.00368
Pubmed ID
Authors

Astrid Zeman, Kevin R. Brooks, Sennay Ghebreab

Abstract

Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 23%
United Kingdom 1 8%
Unknown 9 69%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 31%
Professor > Associate Professor 2 15%
Student > Bachelor 1 8%
Lecturer 1 8%
Student > Postgraduate 1 8%
Other 0 0%
Unknown 4 31%
Readers by discipline Count As %
Psychology 4 31%
Engineering 2 15%
Neuroscience 1 8%
Agricultural and Biological Sciences 1 8%
Unknown 5 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 August 2015.
All research outputs
#13,444,212
of 22,821,814 outputs
Outputs from Frontiers in Human Neuroscience
#4,069
of 7,150 outputs
Outputs of similar age
#124,129
of 263,986 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#95
of 175 outputs
Altmetric has tracked 22,821,814 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,150 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.6. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,986 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 175 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.