Color Variability Constrains Detection of Geometrically Perfect Mirror Symmetry
Symmetry in nature is a result of biological self-organization, driven by evolutionary processes. Detected by the visual systems of various species, from invertebrates to primates, symmetry determines survival relevant choice behaviors and supports adaptive function by reducing stimulus uncertainty....
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/10/6/99 |
| _version_ | 1797488567485726720 |
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| author | Birgitta Dresp-Langley |
| author_facet | Birgitta Dresp-Langley |
| author_sort | Birgitta Dresp-Langley |
| collection | DOAJ |
| description | Symmetry in nature is a result of biological self-organization, driven by evolutionary processes. Detected by the visual systems of various species, from invertebrates to primates, symmetry determines survival relevant choice behaviors and supports adaptive function by reducing stimulus uncertainty. Symmetry also provides a major structural key to bio-inspired artificial vision and shape or movement simulations. In this psychophysical study, local variations in color covering the whole spectrum of visible wavelengths are compared to local variations in luminance contrast across an axis of geometrically perfect vertical mirror symmetry. The chromatic variations are found to delay response time to shape symmetry to a significantly larger extent than achromatic variations. This effect depends on the degree of variability, i.e., stimulus complexity. In both cases, we observe linear increase in response time as a function of local color variations across the vertical axis of symmetry. These results are directly explained by the difference in computational complexity between the two major (<i>magnocellular</i> vs. <i>parvocellular</i>) visual pathways involved in filtering the contrast (<i>luminance</i> vs. <i>luminance and color</i>) of the shapes. It is concluded that color variability across an axis of symmetry proves detrimental to the rapid detection of symmetry, and, presumably, other structural shape regularities. The results have implications for vision-inspired artificial intelligence and robotics exploiting functional principles of human vision for gesture and movement detection, or geometric shape simulation for recognition systems, where symmetry is often a critical property. |
| first_indexed | 2024-03-10T00:05:09Z |
| format | Article |
| id | doaj.art-408fcd02534045c3bc4360e2d2239337 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-10T00:05:09Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-408fcd02534045c3bc4360e2d22393372023-11-23T16:09:44ZengMDPI AGComputation2079-31972022-06-011069910.3390/computation10060099Color Variability Constrains Detection of Geometrically Perfect Mirror SymmetryBirgitta Dresp-Langley0UMR 7357 CNRS, ICube Research Department, Strasbourg University, 67085 Strasbourg, FranceSymmetry in nature is a result of biological self-organization, driven by evolutionary processes. Detected by the visual systems of various species, from invertebrates to primates, symmetry determines survival relevant choice behaviors and supports adaptive function by reducing stimulus uncertainty. Symmetry also provides a major structural key to bio-inspired artificial vision and shape or movement simulations. In this psychophysical study, local variations in color covering the whole spectrum of visible wavelengths are compared to local variations in luminance contrast across an axis of geometrically perfect vertical mirror symmetry. The chromatic variations are found to delay response time to shape symmetry to a significantly larger extent than achromatic variations. This effect depends on the degree of variability, i.e., stimulus complexity. In both cases, we observe linear increase in response time as a function of local color variations across the vertical axis of symmetry. These results are directly explained by the difference in computational complexity between the two major (<i>magnocellular</i> vs. <i>parvocellular</i>) visual pathways involved in filtering the contrast (<i>luminance</i> vs. <i>luminance and color</i>) of the shapes. It is concluded that color variability across an axis of symmetry proves detrimental to the rapid detection of symmetry, and, presumably, other structural shape regularities. The results have implications for vision-inspired artificial intelligence and robotics exploiting functional principles of human vision for gesture and movement detection, or geometric shape simulation for recognition systems, where symmetry is often a critical property.https://www.mdpi.com/2079-3197/10/6/99mirror symmetrylocal shape propertiesluminance contrastcolorshape computing pathwayscomplexity |
| spellingShingle | Birgitta Dresp-Langley Color Variability Constrains Detection of Geometrically Perfect Mirror Symmetry Computation mirror symmetry local shape properties luminance contrast color shape computing pathways complexity |
| title | Color Variability Constrains Detection of Geometrically Perfect Mirror Symmetry |
| title_full | Color Variability Constrains Detection of Geometrically Perfect Mirror Symmetry |
| title_fullStr | Color Variability Constrains Detection of Geometrically Perfect Mirror Symmetry |
| title_full_unstemmed | Color Variability Constrains Detection of Geometrically Perfect Mirror Symmetry |
| title_short | Color Variability Constrains Detection of Geometrically Perfect Mirror Symmetry |
| title_sort | color variability constrains detection of geometrically perfect mirror symmetry |
| topic | mirror symmetry local shape properties luminance contrast color shape computing pathways complexity |
| url | https://www.mdpi.com/2079-3197/10/6/99 |
| work_keys_str_mv | AT birgittadresplangley colorvariabilityconstrainsdetectionofgeometricallyperfectmirrorsymmetry |