da Fonseca, María de los Angeles (2018) Visión en color : análisis estadístico de la absorción de fotones en la retina y sus consecuencias perceptuales. / Color vision : a statistical model of fhoton absortion in the retina and its perceptual consequences. Tesis Doctoral en Física, Universidad Nacional de Cuyo, Instituto Balseiro.
![[img]](/style/images/fileicons/application_pdf.png)
| PDF (Tesis) Disponible bajo licencia Creative Commons: Reconocimiento - No comercial - Compartir igual. Español 6Mb |
Resumen en español
El color es una sensación subjetiva originada en el cerebro, basada en la información que llega por la vía visual sobre la distribución de energía de la luz que incide sobre la pupila. Los fotorreceptores constituyen la primera etapa en el procesamiento neuronal de la información cromática, por lo tanto, es esperable que parte de las características de la visión en color puedan explicarse en términos de la fisiología del proceso de absorción de fotones. Sin embargo, existen numerosas etapas de procesamiento posteriores, todas ellas requeridas para que un observador sea capaz de reportar qué color ve, así como para informar si nota diferencias entre dos estímulos o si los percibe como iguales. Por lo tanto, no hay motivos para creer que basta comprender la fisiología de los fotorreceptores para poder explicar todas las características de la percepción cromática. Para determinar la relevancia del proceso de absorción, en esta tesis modelamos estadísticamente la forma en que los conos de la retina capturan los fotones incidentes. Bajo la suposición de que la estocasticidad del proceso de absorción constituye el factor fundamental que limita la precisión de la percepción cromática, utilizando técnicas estadísticas y de la teoría de la información, predecimos el resultado de diversos experimentos comportamentales reportados en la literatura. La precisión con que se reproducen los resultados experimentales sustenta la hipótesis de que las etapas de procesamiento posteriores operan de manera óptima, o cercana a la óptima, alterando sólo mínima mente las limitaciones impuestas por la etapa de absorción.
Resumen en inglés
Colour is a subjective sensation originated in the brain, based on the information that enters through the visual pathway about the energy distribution of the light impinging on the pupil. Fotoreceptors constitute the first stage in the neuronal processing of chromatic information, so the physiology of the absorption process is expected to be relevant in the understanding of colour vision. There are, however, multiple subsequent processing stages, all of them required for an observer to be able to report the colour of a stimulus, and to determine whether he or she perceives two stimuli as chromatically equal or not. There is no reason, hence, to believe that photoreceptors suffice to explain all the properties of chromatic perception. To determine the relevance of the absorption process, in this thesis we construct a statistical model of the way incident photos are captured by the cones of the retina. Under the assumption that the stochasticity in the absorption process is the fundamental factor limiting the precision of chromatic perception, using statistical and information-theoretical tools, we predict the result of several behavioral experiments reported in the literature. The precision with which the model reproduces the experimental results supports the hypothesis that subsequent processing stages operate optimally, or near optimality, altering only minimally the limitations imposed by the absorption process.
| Tipo de objeto: | Tesis (Tesis Doctoral en Física) |
|---|---|
| Palabras Clave: | Heat; Calor; Information theory; Teoría de información; Vision; Retina; Statistics; Estadística |
| Referencias: | [1] da Fonseca, M., Vattuone, N., Clavero, F., Echeveste, R., Samengo, I. The subjective metric of remembered colors: An information-theoretical analysis of the geometry of human chromatic memory (submitted). PlosOne, 30 (6), 2018. 1 [2] Smeulders, N., Campbell, F. W., Andrews, P. R. The role of delineation and spatial frequency in the perception of the colours of the spectrum. Vision Research, 34 (7), 927–936, 1994. 1, 2 [3] Finger, S. Origins of Neuroscience. Oxford: Oxford, 2001. 1, 2, 6 [4] Hubel, D. Eye, Brain and Vision. New York: Scientific American Library, 1988. 3, 4, 5 [5] Barbur, J. L., Stockman, A. Photopic, mesopic and scotopic vision and changes in visual performance. Encyclopedia of the Eye, 3, 323–331, 2010. 4, 5 [6] Bowmaker, J. K., Dartnall, H. J. Visual pigments of rods and cones in a human retina. The Journal of Physiology, 298 (1), 501–511, 1980. 5 [7] Colorblindor. http://www.color-blindness.com, 2016. Urldate: 2016. 6, 8 [8] Nathans, J., Thomas, D., Hogness, D. Molecular genetics of human color vision: the genes encoding blue, green, and red pigments. 232 (4747), 193–202, 1986. 7 [9] Stockman, A., Brainard, D. H. Handbook of Optics, cap. Color vision mechanisms, p´ags. 1–104. New York: McGraw-Hill, 2009. 7, 8, 27, 48, 66, 75 [10] Jordan, G., Deeb, S. S., Bosten, J. M., Mollon, J. D. The dimensionality of color vision in carriers of anomalous trichromacy. Journal of Vision, 8 (10), 1–19, 2010. 8, 52 [11] Wyszecki, G., Stiles, W. S. Color Science: Concepts and Methods, Quantitative Data and Formulae. New York: Wiley Interscience, 2000. 8, 9, 10, 12, 19, 21, 31, 45, 54, 56, 61, 62, 73, 74 [12] Kaiser, P. K. The joy of visual perception. http://www.yorku.ca/eye/, 1996. Urldate: Feb 2017. 9 [13] Judd, D. B., Vos, J. J. Modified cie 2-deg photopic luminosity curve. http://www.cvrl.org/database/text/lum/vljv.htm, 1978. Urldate: Feb 2017. 10 [14] Sharpe, L. T., Stockman, A., Jagla, W., Jgle, H. A luminous efficiency function, v*(λ), for daylight adaptation. Journal of Vision, 5 (11), 3, 2005. 10, 49 [15] Stiles, W. S., Burch, J. M. Stiles & burch 2-deg color matching functions. http://www.cvrl.org/, 1955. 11, 77 [16] Fundamental chromaticity diagram with physiological axes. http://www.cvrl.org/, 2006. 13 [17] Ciexy1931. https://commons.wikimedia.org/wiki/File:CIExy1931.png, 2005. Urldate: Jun 2005. 13 [18] Malacara, D. Color Vision and Colorimetry: Theory and Applications. Bellingham: Spie, 2011. 14, 21, 31, 61, 62, 74 [19] Derrington, A. M., Krauskopf, J., Lennie, P. Chromatic mechanisms in lateral geniculate nucleus of macaque. Journal of Physiology, 357 (1), 241–265, 1984. 14, 58, 62 [20] Hansen, T., Gegenfurtner, K. R. Classification images for chromatic signal. Journal of the Optical Society of America. A, Optics, image science, and vision, 22, 2081–9, 11 2005. 15 [21] Wright, W. D., Pitt, F. H. G. Hue discrimination in normal colour vision. Proceedings of the Physical Society, 46 (3), 459–473, 1934. 16, 64 [22] Pokorny, J., Smith, V. C. Wavelength discrimination in the presence of added chromatic fields. Journal of the Optical Society of America, 60 (4), 562–569, 1970. 16 [23] MacAdam, D. L. Visual sensitivities to color differences in daylight. Journal of the Optical Society of America, 32 (5), 247–274, 1942. 16, 57, 64 [24] Krauskopf, J., Gegenfurtner, K. Color discrimination and adaptation. Vision Research, 32 (11), 2165–2175, 1992. 17, 24, 29, 58 [25] Amari, S. I., Nagaoka, H. Methods in information Geometry. Oxford: Oxford, 2000. 22, 23, 47, 75 [26] Cover, T., Thomas, J. A. Elements of Information Theory. New York: Wiley, 1991. 24 [27] Klauke, S., Wachtler, T. Tilt in color space: Hue changes induced by chromatic surrounds. Journal of Vision, 15 (13), 17, 2015. 24 [28] Pant, D. R., Farup, I. Riemannian formulation and comparison of color difference formulas. Color Research and Application, 37 (6), 429 – 440, 2011. 26 [29] Vorobyev, M., Osorio, D. Receptor noise as a determinant of colour thresholds. Proceedings of the Royal Society of London B: Biological Sciences, 265 (1394), 351–358, 1998. 26 [30] da Fonseca, M., Samengo, I. Derivation of human chromatic discrimination ability from an information-theoretical notion of distance in color space. Neural Computation, 28 (12), 2628–2655, 2016. 32, 37, 65, 70 [31] Zhaoping, L., Geisler, W. S., May, K. A. Human wavelength discrimination of monochromatic light explained by optimal wavelength decoding of light of unknown intensity. PLoS ONE, 6 (5), e19248, 2011. 36, 48, 65 [32] Cram´er, H. A contribution to the theory of statistical estimation. Scandinavian Actuarial Journal, 1946 (1), 458–463, 1946. 47, 65 [33] Dayan, P., Abbot, L. F. Theoretical Neuroscience. Computational and Mathematical Modeling of Neural Systems. Cambridge: MIT Press, 2001. 48 [34] Hofer, H., Carroll, J., Neitz, J., Neitz, M., Williams, D. R. Organization of the human trichromatic cone mosaic. Journal of Neuroscience, 25 (42), 9669 – 9679, 2005. 49 [35] Roorda, A., Williams, D. D. The arrangement of the three cone classes in the living human eye. Nature, 397, 520–522, 1999. 49 [36] Sperling, H. G., Harwerth, R. S. Red-green cone interactions in the incrementthreshold spectral sensitivity of primates. Science, 172 (3979), 180–184, 1971. 50 [37] Hart, N., Partridge, J., Bennett, A., Cuthill, I. Visual pigments, cone oil droplets and ocular media in four species of estrildid finch. Journal of Comparative Physiology A, 186 (7), 681–694, Aug 2000. 52 [38] Rose, A. The sensitivity performance of the human eye on an absolute scale∗. J. Opt. Soc. Am., 38 (2), 196–208, Feb 1948. 54 92 Bibliograf´ıa [39] DeVries, H. L. The quantum character of light and its bearing upon threshold of vision, the differential sensitivity and the visual acuity of the eye. Physica, 7 (10), 553 – 564, Jul 1943. 54 [40] Rovamo, J. M., KanKaanp¨a¨a, M. I., Hallikainen, J. Spatial neural modulation transfer function for human foveal visual system for equiluminous chromatic gratings. Vision Research, 41 (13), 1659–1667, jun 2001. 54 [41] Duchi, J. C. Derivations for linear algebra and optimization. resource document. http://ai. stanford.edu/˜jduchi/projects/general notes.pdf, 2014. Urldate: Jan 2016. 56 [42] Koenderink, J. J., Van Doorn, A. J. Colour Perception: Mind and the physical world, cap. Perspectives on colour space, p´ags. 1–56. Oxford: Oxford University Press, 2003. 61 [43] Munsell, A. H. A pigment color system and notation. The American Journal of Psychology, 23 (2), 236–244, 1912. 61 [44] Landa, E. R., Fairchild, M. D. Charting color from the eye of the beholder. American Scientist, 93 (5), 436443, 2005. 61 [45] MacAdam, D. L. On the geometry of color space. Journal of the Franklin Institute, 238 (5), 195–201, 1944. 62 [46] Gravesen, J. The metric of colour space. Graphical Models, 82, 77–86, 2015. 63 [47] Holtsmark, T. Colour discrimination and hue. Nature, 224 (5217), 366–367, 1969. 64 [48] Atick, J. J. Could information theory provide an ecological theory of sensory perception? network: Computation in neural systems. Network: Computation in neural systems, 3 (2), 213–251, 1992. 66, 67 [49] Bruton, D. Rgb values for visible wavelengths. http://www.physics.sfasu.edu/ astro/color/spectra.html, 1996. Urldate: Feb 1996. 69 [50] Silberstein, L. Investigations on the intrinsic properties of the color domain. Journal of the Optical Society of America, 33 (1), 1–10, 1943. 69 [51] van der Twer, T., MacLeod, D. I. A. Optimal nonlinear codes for the perception of natural colours. Network: Computation in Neural Systems, 12 (3), 395–407, 2001. 70 [52] MacLeod, D. I. A., van der Twer, T. The pleistochrome: Optimal opponent codes for natural colours. Colour Perception: Mind and the Physical World, 2003. [53] MacLeod, D. A. Colour discrimination, colour constancy, and natural scene statistics. Normal and defective colour vision, p´ags. 189–218, 2003. 70 [54] Laparra, V., Jim´enez, S., Camps-Valls, G., Malo, J. Nonlinearities and adaptation of color vision from sequential principal curves analysis. Neural Computation, 24 (10), 2751–2788, 2012. 70 [55] Laparra, V., Malo, J. Visual aftereffects and sensory nonlinearities from a single statistical framework. Frontiers in Human Neuroscience, 9, 557, 2015. 70 [56] Laughlin, S. B. A simple coding enhances a neurons information capacity. Zeitschrift fr Naturforschung, 36c, 91012, 1981. 70 [57] Srinivasan, M. V., Laughlin, S. B., Dubs, A. Predictive coding: A fresh view of inhibition in the retina. Proceedings of the Royal Society of London B, 216 (1205), 427–459, 1982. [58] Laughlin, S. B. Matching coding to scenes to enhance efficiency. Physical and Biological Processing of Images, 11, 42–52, 1983. [59] Bell, A. J., Sejnowski, T. J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7 (6), 1129–1159, 1995. 70 [60] Malo, J., Laparra, V. Psychophysically tuned divisive normalization approximately factorizes the pdf of natural images. Neural Computation, 22 (12), 3179–3206, 2010. 70 [61] Laparra, V., Mu˜noz Mar´ı, J., Malo, J. Divisive normalization image quality metric revisited. J. Opt. Soc. Am. A, 27 (4), 852–864, Apr 2010. 70 [62] Berardino, A., Laparra, V., Ball, J., Simoncelli, E. P. Eigen-distortions of hierarchical representations. Advances in Neural Information Processing Systems 30 (NIPS 2017) pre-proceedings, 30, 2017. 70 [63] Kullback, S. Information theory and statistics. New York: John Wiley and Sons, 1959. 75 [64] Thornton, W. A. Spectral sensitivities of the normal human visual system, colormatching functions and their principles, and how and why the two sets should coincide. Color research and application, 24, 139156, 1999. 80 [65] Brill, M. H., Worthey, J. A. Color matching functions when one primary wavelength is changed. Color research and application, 32, 2224, 2007. [66] Worthey, J. A. Vectorial color. color research and application. Color research and application, 37, 394409, 2012. 80 |
| Materias: | Medicina > Física médica Medicina > Neurociencias |
| Divisiones: | Gcia. de área de Investigación y aplicaciones no nucleares > Gcia. de Física > Física médica |
| Código ID: | 785 |
| Depositado Por: | Tamara Cárcamo |
| Depositado En: | 24 Feb 2021 12:48 |
| Última Modificación: | 24 Feb 2021 12:48 |
Personal del repositorio solamente: página de control del documento
