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An exponential filter model predicts lightness illusions.

Zeman A, Brooks KR, Ghebreab S - Front Hum Neurosci (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Science, ARC Centre of Excellence in Cognition and its Disorders, Macquarie University Sydney, NSW, Australia ; Commonwealth Scientific and Industrial Research Organisation Marsfield, NSW, Australia ; Perception in Action Research Centre, Macquarie University Sydney, NSW, Australia.

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.

No MeSH data available.


Related in: MedlinePlus

Simultaneous Contrast vs. White's Effect. Albedo of gray target patches in Simultaneous Contrast shift away from background, demonstrating contrast. Targets in White's Effect shift toward surrounding context, demonstrating assimilation. Increasing spatial frequency increases the effect in both cases.
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Figure 1: Simultaneous Contrast vs. White's Effect. Albedo of gray target patches in Simultaneous Contrast shift away from background, demonstrating contrast. Targets in White's Effect shift toward surrounding context, demonstrating assimilation. Increasing spatial frequency increases the effect in both cases.

Mentions: Lightness is the perceived reflectance of a surface, which can vary greatly according to surrounding context, as demonstrated by lightness illusions (see Kingdom, 2011 for a recent review). One clear and well-known example is the Simultaneous Contrast Illusion (SCI), where a gray target patch is perceived as lighter when surrounded by a black background and darker when surrounded by a white background (Chevreul, 1839) (Figure 1 left). The SCI demonstrates the contrast phenomenon, where lightness shifts away from surrounding luminance values, luminance being the amount of light that reaches the eye. Under other circumstances, lightness can shift toward the luminance values of bordering areas—a phenomenon known as assimilation1. This is effectively demonstrated by a version of White's Illusion (White, 1979), where the test patches are not as wide as they are tall (Figure 1 right).


An exponential filter model predicts lightness illusions.

Zeman A, Brooks KR, Ghebreab S - Front Hum Neurosci (2015)

Simultaneous Contrast vs. White's Effect. Albedo of gray target patches in Simultaneous Contrast shift away from background, demonstrating contrast. Targets in White's Effect shift toward surrounding context, demonstrating assimilation. Increasing spatial frequency increases the effect in both cases.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4478851&req=5

Figure 1: Simultaneous Contrast vs. White's Effect. Albedo of gray target patches in Simultaneous Contrast shift away from background, demonstrating contrast. Targets in White's Effect shift toward surrounding context, demonstrating assimilation. Increasing spatial frequency increases the effect in both cases.
Mentions: Lightness is the perceived reflectance of a surface, which can vary greatly according to surrounding context, as demonstrated by lightness illusions (see Kingdom, 2011 for a recent review). One clear and well-known example is the Simultaneous Contrast Illusion (SCI), where a gray target patch is perceived as lighter when surrounded by a black background and darker when surrounded by a white background (Chevreul, 1839) (Figure 1 left). The SCI demonstrates the contrast phenomenon, where lightness shifts away from surrounding luminance values, luminance being the amount of light that reaches the eye. Under other circumstances, lightness can shift toward the luminance values of bordering areas—a phenomenon known as assimilation1. This is effectively demonstrated by a version of White's Illusion (White, 1979), where the test patches are not as wide as they are tall (Figure 1 right).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Science, ARC Centre of Excellence in Cognition and its Disorders, Macquarie University Sydney, NSW, Australia ; Commonwealth Scientific and Industrial Research Organisation Marsfield, NSW, Australia ; Perception in Action Research Centre, Macquarie University Sydney, NSW, Australia.

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.

No MeSH data available.


Related in: MedlinePlus