An exponential filter model predicts lightness illusions
Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a grey patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target p...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-06-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00368/full |
| _version_ | 1818113664235864064 |
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| author | Astrid eZeman Astrid eZeman Astrid eZeman Kevin R. Brooks Kevin R. Brooks Sennay eGhebreab Sennay eGhebreab |
| author_facet | Astrid eZeman Astrid eZeman Astrid eZeman Kevin R. Brooks Kevin R. Brooks Sennay eGhebreab Sennay eGhebreab |
| author_sort | Astrid eZeman |
| collection | DOAJ |
| description | Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a grey patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves towards that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (2007) 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. |
| first_indexed | 2024-12-11T03:38:25Z |
| format | Article |
| id | doaj.art-df070c44cae74f188d0df0d23109fad3 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-11T03:38:25Z |
| publishDate | 2015-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-df070c44cae74f188d0df0d23109fad32022-12-22T01:22:12ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612015-06-01910.3389/fnhum.2015.00368123013An exponential filter model predicts lightness illusionsAstrid eZeman0Astrid eZeman1Astrid eZeman2Kevin R. Brooks3Kevin R. Brooks4Sennay eGhebreab5Sennay eGhebreab6Macquarie UniversityCommonwealth Scientific and Industrial Research Organisation (CSIRO)Macquarie UniversityMacquarie UniversityMacquarie UniversityUniversity of AmsterdamUniversity of AmsterdamLightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a grey patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves towards that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (2007) 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.http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00368/fullcontrastModelIllusionlightnessfilterassimilation |
| spellingShingle | Astrid eZeman Astrid eZeman Astrid eZeman Kevin R. Brooks Kevin R. Brooks Sennay eGhebreab Sennay eGhebreab An exponential filter model predicts lightness illusions Frontiers in Human Neuroscience contrast Model Illusion lightness filter assimilation |
| title | An exponential filter model predicts lightness illusions |
| title_full | An exponential filter model predicts lightness illusions |
| title_fullStr | An exponential filter model predicts lightness illusions |
| title_full_unstemmed | An exponential filter model predicts lightness illusions |
| title_short | An exponential filter model predicts lightness illusions |
| title_sort | exponential filter model predicts lightness illusions |
| topic | contrast Model Illusion lightness filter assimilation |
| url | http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00368/full |
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