Neuroscience and Behavioral Physiology

, Volume 39, Issue 6, pp 569–580 | Cite as

Analysis of the studies of the perception of fragmented images: global description and perception using local features

  • Yu. E. Shelepin
  • V. N. Chikhman
  • N. Foreman

This report presents an analysis of studies of the visual perception of fragmented images. Two approaches to the description of brain functioning during the solution of this task are considered: studies of the perception of the statistical properties of global whole images and studies of the perception of images in terms of local higher-order informative features. These approaches describe two different mechanisms which the human brain may use for recognizing incomplete images. Studies performed using the Gollin test (measurement of recognition thresholds for fragmented outline images during gradual construction of the outline) are given most attention. In solving this task, the visual system extracts the statistical properties of the whole image. Local higher-order informative features are used by the brain as additional sources of information about the image. The role of this source increases on learning a given alphabet of stimuli. In accordance with a matched filtration model, the fragmented images used in the Gollin test are perceived as a whole structure, compared with a reference which is stored in memory and selected using the selective attention mechanism. At the primary filtration step and the matched filtration step, the recognition thresholds of images in the Gollin test reflect the processes of extracting the signal from noise. The Gollin test can be used as a differential tool for the diagnosis of different types of cognitive impairments.


visual perception fragmented images gestalt image recognition signal/interference matched filtration 


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  1. 1.
    K. V. Bardin, The Problem of Sensitivity Thresholds and Psychophysical Methods [in Russian], Nauka, Moscow (1976).Google Scholar
  2. 2.
    V. D. Glezer and I. Tsukkerman, Information and Vision [in Russian], Academy of Sciences of the USSR Press, Moscow, Leningrad (1961).Google Scholar
  3. 3.
    A. A. Deshkovich, N. N. Krasil’nikov, A. V. Merkul’ev, S. V. Muras’eva, M. M. Odinak, S. V. Pronin, and Yu. E. Shelepin, “The Gollin test,” in: Current Approaches to the Diagnosis and Treatment of Nervous and Mental Diseases [in Russian], Chief Military Medical Directorate, Ministry of Education of the Russian Federation, St. Petersburg (2000), p. 587.Google Scholar
  4. 4.
    A. A. Deshkovich, A. V. Merkul’ev, V. N. Chikhman, and Yu. E. Shelepin, “Perception of fragmented images and the diagnosis of visual system lesions,” in: Proceedings of Conference “Combat Injuries to the Visual Organs” [in Russian], VMA Press, St. Petersburg (2003), p. 161.Google Scholar
  5. 5.
    V. M. Kamenkovich and I. A. Shevelev, “Latent periods of recognition of geometrical figures by humans at different levels of masking of their sides and angles,” Fiziol. Cheloveka, 32, No. 2, 5–9 (2006).PubMedGoogle Scholar
  6. 6.
    N. N. Krasil’nikov and Yu. E. Shelepin, “Masking as a result of mismatching filtration in the human visual system,” Fiziol. Cheloveka, 22, No. 5, 99–103 (1996).PubMedGoogle Scholar
  7. 7.
    N. N. Krasil’nikov and Yu. E. Shelepin, “A functional model of vision,” Optichesk. Zh., 64, No. 2, 72–82 (1997).Google Scholar
  8. 8.
    V. V. Lavrov and A. V. Rudinskii, “Recognition of fragmented images,” Sensor. Sistemy, 4, 317–324 (2004).Google Scholar
  9. 9.
    D. Marr, An Informational Approach to Studies of the Representation and Processing of Visual Images [in Russian], Radio i Svyaz’, Moscow (1987).Google Scholar
  10. 10.
    A. V. Merkul’ev, Yu. E. Shelepin, V. N. Chikhman, S. V. Pronin, and N. Foreman, “Optical geometrical characteristics and perception thresholds of fragmented outline figures,” Ros. Fiziol. Zh. im. I. M. Sechenova, 89, No. 6, 731–737 (2003).Google Scholar
  11. 11.
    A. V. Merkul’ev, S. V. Pronin, L. A. Semenov, N. Foreman, V. N. Chikhman, and Yu. E. Shelepin, “Signal:noise ratio thresholds in the perception of fragmented images,” Ros. Fiziol. Zh. im. I. M. Sechenova, 90, No. 11, 1348–1355 (2004).Google Scholar
  12. 12.
    A. V. Merkul’ev, “Optical-geometrical characteristics of fragmented images and thresholds for their intact perception on repeat testing,” Ros. Fiziol. Zh. im. I. M. Sechenova, 93, No. 8, 886–897 (2007).Google Scholar
  13. 13.
    N. N. Pavlov, S. A. Koskin, and Yu. E. Shelepin, “Effects of spatial discretization and filtration of image elements on the possibility of combining them into an image,” Sensor. Sistemy, 3, No. 4, 417–422 (1989).Google Scholar
  14. 14.
    N. F. Podvigin, T. V. Bagaeva, and D. N. Podvigina, “Selective autosynchronization of spike streams in visual system neural networks,” Dokl. Ros. Akad. Nauk., 400, No. 1, 1–3 (2005).Google Scholar
  15. 15.
    I. Tonkonogii and I. Tsukkerman, “Use of images distorted by fluctuations for studies of impairments to visual gnosis,” Zh. Nevropatol. Psikhatr. im. S. S. Korsakova, 63, No. 2, 236–239 (1963).Google Scholar
  16. 16.
    M. I. Trifonov, Yu. E. Shelepin, N. N. Pavlov, V. G. Sharevich, and A. V. Popov, “Studies of the frequency-contrast characteristics of the visual system in conditions of interference,” Fiziol. Cheloveka, 16, No. 2, 41–45 (1990).PubMedGoogle Scholar
  17. 17.
    U. U. Tsukkerman, “The statistical structure of images and characteristics of visual perception,” in: Information Processing in the Visual System [in Russian], Nauka, Leningrad (1975), pp. 213–215.Google Scholar
  18. 18.
    I. V. Tsukkerman, “Matching of spatial-frequency filters in the visual analyzer with image statistics,” Biofizika, 23, No. 6, 1108–1109 (1978).PubMedGoogle Scholar
  19. 19.
    I. A. Shevelev, B. M. Kamenkovich, and G. A. Sharaev, “The relative values of lines and angles of geometrical figures for their recognition by humans,” Zh. Vyssh. Nerv. Deyat., 50, No. 3, 403 (2000).Google Scholar
  20. 20.
    I. Shevelev, V. Kamenkovich, N. Lazareva, R. Novikova, A. Tikhomirov, and G. Sharaev, “Psychophysical and neurophysiological studies of the recognition of incomplete images,” Sensor. Sistemy, 17, No. 4, 339–346 (2003).Google Scholar
  21. 21.
    I. A. Shevelev, V. M. Kamenkovich, N. A. Lazareva, G. A. Sharaev, R. V. Novikova, and A. S. Tikhomirov, “Perception of incomplete figures and responses of striate neurons to second-order image features,” in: Proceedings of the 18th Congress of the I. P. Pavlov Physiological Society, Kazan’ (2001), pp. 275–276.Google Scholar
  22. 22.
    Yu. E. Shelepin, V. B. Makulov, N. N. Krasil’nikov, V. N. Chikhman, S. V. Pronin, V. F. Danilichev, and S. A. Koskin, “Iconics and methods of assessing the functional capacities of the visual system,” Sensor. Sistemy, 12, No. 3, 319–328 (1998).Google Scholar
  23. 23.
    Yu. E. Shelepin, “Perception of fragmented images,” in: The Organization and Plasticity of the Cerebral Cortex [in Russian], Research Institute of the Brain, Russian Academy of Medical Sciences, Moscow (2001), p. 103.Google Scholar
  24. 24.
    Yu. E. Shelepin, V. V. Volkov, and L. N. Kolesnikova, The Least Action Principle in Vision. Principles and Mechanisms of Human Brain Activity [in Russian], Nauka, Leningrad (1985).Google Scholar
  25. 25.
    Yu. Shelepin and N. Krasil’nikov, “The least action principle, visual physiology, and conditioned reflex theory,” Ros. Fiziol. Zh. im. I. M. Sechenova, 89, No. 6, 725–730 (2003).Google Scholar
  26. 26.
    Yu. E. Shelepin, B. N. Chikhman, A. K. Kharauzov, V. M. Bondarko, and O. A. Vakhrameeva, “Perception of fragmented images,” Ros. Fiziol. Zh. im. I. M. Sechenova, 90, No. 8, 355 (2004).Google Scholar
  27. 27.
    F. Attneave, “Some information aspects of visual perception,” Psychol. Rev., 61, 183–198 (1954).PubMedCrossRefGoogle Scholar
  28. 28.
    F. Attneave, “Symmetry, information and memory for patterns,” Amer. J. Psychol., 68, 209–222 (1955).PubMedCrossRefGoogle Scholar
  29. 29.
    H. Barlow, “Summation and inhibition in the frog’s retina,” J. Physiol. (London), 119, 69–88 (1953).Google Scholar
  30. 30.
    A. M. Bentley and J. B. Deregowski, “Pictorial experience as a factor in the recognition of incomplete figures,” Appl. Cogn. Psychol., 1, 209–216 (1987).CrossRefGoogle Scholar
  31. 31.
    I. Biederman, “Recognition-by-components: A theory of human understanding,” Psychol. Rev., 94, 115–147 (1987).PubMedCrossRefGoogle Scholar
  32. 32.
    I. Biederman and E. E. Cooper, “Priming contour-deleted images: Evidence for intermediate representations in visual object recognition,” Cogn. Psychol., 23, 393–419 (1991).PubMedCrossRefGoogle Scholar
  33. 33.
    J. L. Bradshaw and J. B. Mattingley, Clinical Neuropsychology. Behavioral Brain Science, Academic Press, New York (1995).Google Scholar
  34. 34.
    A. S. Bregman, “Asking the ‘what for’ question in auditory perception,” in: Perception Organization, M. Kubovy and J. Pomerantz (eds.), Lawrence Erlbaum Associates, Mahway, New York (1981), pp. 99–118.Google Scholar
  35. 35.
    V. Bruce, P. Green, and M. Georgeson, Visual Perception, Psychology Press, Hove, Sussex (1996).Google Scholar
  36. 36.
    T. Caelli, “On discriminating visual textures and images,” Percept. Psychophysics, 31, 149–159 (1982).Google Scholar
  37. 37.
    F. Campbell and J. Robson, “Application of Fourier analyses to the visibility of gratings,” J. Physiol., 197, 551–556 (1968).PubMedGoogle Scholar
  38. 38.
    V. Chikhman, Y. Shelepin, S. Pronin, A. Harausov, N. Krasilnikov, and V. Makulov, “Electrophysiological measurements of the natural image distortion,” SPIE, No. 3299, 510–518 (1998).Google Scholar
  39. 39.
    V. N. Chikhman, Y. E. Shelepin, N. Foreman, A. V. Merkuljev, and N. N. Krasilnikov, “The Gollin test and the optical properties of incomplete figures at threshold,” Perception, 30, Supplement, 89 (2001).Google Scholar
  40. 40.
    V. Chikhman, Y. Shelepin, S. Pronin, V. Lavrov, and Y. Pushkarev, “Influence of anxiety on recognition of fragmented contour images by human observers,” Perception, 30, Supplement, 88, (2001).Google Scholar
  41. 41.
    V. N. Chikhman, Y. E. Shelepin, N. Foreman, A. V. Merkuljev, and S. Pronin, “Incomplete figure perception and invisible masking,” Perception, 35, 1441–1457 (2006).PubMedCrossRefGoogle Scholar
  42. 42.
    S. Corkin, “Some relationships between global amnesias and the memory impairments in Alzheimer’s Disease,” in: Alzheimer’s Disease. A Report of Progress, S. Corkin, K. Davis, J. Growdon, E. Usdin, and R. Wurtman (eds.), Raven Press, New York (1982), pp. 149–164.Google Scholar
  43. 43.
    J. De Winter and J. Wagemans, “Contour-based object identification and segmentation: Stimuli, norms and data, and software tools,” Behavior Research Methods, Instrumentation. Computers, 36, 604–624 (2004).Google Scholar
  44. 44.
    D. V. DiGiulio, M. Seidenberg, D. S. O’Leary, and N. Raz, “Procedural and declarative memory: A development study,” Brain Cogn., 25, 79–91 (1994).PubMedCrossRefGoogle Scholar
  45. 45.
    D. J. Field, “Relations between the statistics of natural images and the response properties of cortical cells,” J. Opt. Soc. Amer., A4, 2379–2394 (1987).CrossRefGoogle Scholar
  46. 46.
    D. J. Field, “What is the goal of sensory coding?” Neural Computation, 6, 559–601 (1994).CrossRefGoogle Scholar
  47. 47.
    D. J. Field and N. Brady, “Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes,” Vision. Res., 37, 3367–3383 (1997).PubMedCrossRefGoogle Scholar
  48. 48.
    D. Field and A. Hayes, “Contour integration and the lateral connections of V1 neurons,” in: The Visual Neurosci., M. Chalupa and J.Werner (eds.), MIT Press, Cambridge, MA (2004), pp. 1069–1079.Google Scholar
  49. 49.
    N. Foreman, “Correlates of performance on the Gollin and Mooney tests of visual closure,” J. Gen. Psychol., 118, 13–20 (1991).PubMedGoogle Scholar
  50. 50.
    N. Foreman and R. Hemmings, “The Gollin incomplete figure test: a flexible, computerized version,” Perception, 16, 543–548 (1987).PubMedCrossRefGoogle Scholar
  51. 51.
    P. Fries, J. Schroder, P. Roelfsema, W. Singer, and A. Engel, “Oscillatory neuronal synchronization in primary visual cortex as a correlate of stimulus selection,” J. Neurosci., 22, No. 9, 3739–3754 (2002).PubMedGoogle Scholar
  52. 52.
    L. Ghent, “Perception of overlapping and embedded figures by children of different ages,” Amer. J. Psychol., 69, 575–587 (1956).PubMedCrossRefGoogle Scholar
  53. 53.
    A. Ghosh and N. Petkov, “Robustness of shape descriptors to incomplete contour representations,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 27, 1793–1804 (2005).CrossRefGoogle Scholar
  54. 54.
    A. Giersch, G. W. Humphreys, M. Boucart, and I. Kovacs, “The computation of occluded contours in visual agnosia: Evidence for early computation prior to shape binding and figure-ground coding,” Cogn. Neuropsychology, 17, 731–759 (2000).CrossRefGoogle Scholar
  55. 55.
    A. P. Ginsburg, “Is the illusory triangle physical or imaginary?” Nature, 257, 219–220 (1975).PubMedCrossRefGoogle Scholar
  56. 56.
    A. P. Ginsburg and P. W. Evans, “Predicting visual illusions from filtered images based upon biological data,” J. Opht. Soc. Amer., 69, 1443 (1979).Google Scholar
  57. 57.
    A. Ginsburg, “Spatial filtering and visual form perception,” in: Handbook of Perception and Human Performance, K. Boff (ed.), John Wiley and Sons, New York (1986), pp. 99–109.Google Scholar
  58. 58.
    V. D. Glezer, “Vision and Mind. Modelling mental functioning,” in: Vision and Thought [in Russian], Nauka, Leningrad (1996).Google Scholar
  59. 59.
    E. S. Gollin, “Developmental studies of visual recognition of incomplete objects,” Perceptual Motor Skills, 11, 289–298 (1960).CrossRefGoogle Scholar
  60. 60.
    T. Gruber, M. Muller, and A. Keil, “Modulation of induced gamma band responses in a perceptual learning task in the human EEG,” J. Cogn. Neurosci., 14, 732–744 (2002).PubMedCrossRefGoogle Scholar
  61. 61.
    R. Hess and D. Field, “Integration of contours: new insights,” Trends Cogn. Sci., 3, No. 12, 480–486 (1999).PubMedCrossRefGoogle Scholar
  62. 62.
    R. Hess, A. Hayes, and D. Field, “Contour integration and cortical processing,” J. Physiol. Paris, 97, 105–119 (2003).PubMedCrossRefGoogle Scholar
  63. 63.
    D. Hubel and T. Wiesel, “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex,” J. Physiol. (London), 166, 106–154 (1962).Google Scholar
  64. 64.
    D. H. Hubel and T. N. Wiesel, “Receptive fields and functional architecture of monkey striate cortex,” J. Physiol. (London), 195, 215–243 (1968).Google Scholar
  65. 65.
    D. Hubel and T. Wiesel, “Clustered intrinsic connections in cat visual cortex,” J. Neurosci., 3, 1116–1133 (1983).Google Scholar
  66. 66.
    J. Hummel and I. Biederman, “Dynamic binding in a neural network for shape recognition,” Psychol. Rev., 99, 480–517 (1992).PubMedCrossRefGoogle Scholar
  67. 67.
    G. Kanizsa, Organization in Vision, Praeger, New York (1979).Google Scholar
  68. 68.
    P. Kellman and E. Spelke, “Perception of partly occluded objects in infancy,” Cogn. Psychol., 15, No. 4, 483–524 (1983).PubMedCrossRefGoogle Scholar
  69. 69.
    K. Koffka, Principles of Gestalt Psychology, New York (1935).Google Scholar
  70. 70.
    W. Kohler, Gestalt Psychology, New York (1947).Google Scholar
  71. 71.
    W. Kuhler, “Max Wertheimer 1880–1943,” Psychol. Rev. Psychol., 51, 143–146 (1944).CrossRefGoogle Scholar
  72. 72.
    J. J. Kulikowski and A. G. Robson, “Spatial, temporal and chromatic channels: Electrophysiological foundations,” J. Opt. Technol., 66, 797–808 (1999).Google Scholar
  73. 73.
    K. Lindfield and A. Wingfield, “An experimental and computational analysis of age differences in the recognition of fragmented pictures: inhibitory connections versus speed of processing,” Exptl. Aging Res., 25, No. 3, 223–242 (1999).CrossRefGoogle Scholar
  74. 74.
    R. A. McCarthy and E. K. Warrington, Cognitive Neuropsychology: A clinical Introduction, Academic Press, San Diego, CA (1990), Vol. 31, p. 302.Google Scholar
  75. 75.
    J. L. Mack, M. B. Patterson, A. H. Schnell, and P. J. Whitehouse, “Performance of subjects with probable Alzheimer Disease and normal elderly controls on the Gollin Incomplete Pictures Test,” Percept. Motor Skills, 77, 951–969 (1993).PubMedGoogle Scholar
  76. 76.
    D. Marr, “Analysis of occluding contour,” Proc. Roy. Soc., B197, 441–475 (1977).Google Scholar
  77. 77.
    G. N. Martin, Human Neuropsychology, Prentice-Hall, Harlow, Essex, Second Edition (2006).Google Scholar
  78. 78.
    B. Mathes and M. Fahle, “Closure facilitates contour integration,” Vision Res., 47, 818–827 (2007).PubMedCrossRefGoogle Scholar
  79. 79.
    K. May and R. Hess, “Dynamics of snakes and ladders,” J. Vision, 7, No. 12, 109 (2007).CrossRefGoogle Scholar
  80. 80.
    C. M. Mooney, “Age in the development of closure ability in children,” Can. J. Psychol., 2, 219–226 (1957).Google Scholar
  81. 81.
    F. S. Murray and J. M. Szymczyk, “Effects of distinctive features on the recognition of incomplete figures,” Dev. Psychol., 14, 356–362 (1978).CrossRefGoogle Scholar
  82. 82.
    B. A. Olshausen and F. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature, 381, 607–609 (1996).PubMedCrossRefGoogle Scholar
  83. 83.
    B. A. Olshausen and D. J. Field, “Sparse coding with an overcomplete basis set: A strategy employed by V1?” Vision Res., 37, 3311–3325 (1997).PubMedCrossRefGoogle Scholar
  84. 84.
    B. A. Olshausen and D. J. Field, “Sparse coding of sensory inputs,” Curr. Opin. Neurobiol., 14, 481–487 (2004).PubMedCrossRefGoogle Scholar
  85. 85.
    B. A. Olshausen and D. J. Field, “What is the other 85% of V1 doing?” in: 23 Problems in Systems Neuroscience, T. J. Sejnowski and L. van Hemmen (eds.), Oxford University Press, Oxford (2004).Google Scholar
  86. 86.
    M. B. Patterson, J. L. Mack, and A. H. Schnell, “Performance of elderly and young normals on the Gollin Incomplete Pictures Test,” Percept. Motor Skills, 89, 663–664 (1999).PubMedCrossRefGoogle Scholar
  87. 87.
    L. N. Piotrowski and F. W. Campbell, “A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase,” Perception, 11, 337–346 (1982).PubMedCrossRefGoogle Scholar
  88. 88.
    W. Poppelreuter, Die Psychischen Schädigungen durch Kopfschuss im Kriege 1914–1916, Voss, Leipzig (1917).Google Scholar
  89. 89.
    W. K. Pratt, Digital Image Processing [Russian translation], Mir, Moscow (1982).Google Scholar
  90. 90.
    J. M. Reales, S. Ballesteros, and E. Garcia, “Visual word identification thresholds for the 260 fragmented words of the Snodgrass and Vanderwart pictures in Spanish,” Behav. Res. Methods. Instruments. Computers, 34, 276–285 (2002).Google Scholar
  91. 91.
    R. Rensink and J. Enns, “Early completion of occluded objects,” Vision. Res., 38, 2489–2505 (1998).PubMedCrossRefGoogle Scholar
  92. 92.
    P. Servos, E. Olds, P. Planetta, and G. Humphrey, “Recognizing partially visible objects,” Vision Res., 45, 1807–1814 (2005).PubMedCrossRefGoogle Scholar
  93. 93.
    Y. E. Shelepin and N. N. Pavlov, “Spatial discreteness, image filtration, and Gestalt construction,” Perception, 12, No. 4, 537 (1989).Google Scholar
  94. 94.
    Y. E. Shelepin, N. N. Krasilnikov, O. I. Krasilnikova, and V. N. Chikhman, “What visual perception model is optimal in terms of signalto-noise ratio?” in: Proceedings of SPIE. Medical Imaging, San Diego, CA (2000), No. 3981, pp. 161–169.Google Scholar
  95. 95.
    Y. Shelepin, O. Vahrameeva, A. Harauzov, S. Pronin, N. Krasilnikov, N. Foreman, and V. Chikhman, “The recognition of incomplete contour and half-tone figures,” Perception, 33, 85 (2004).Google Scholar
  96. 96.
    D. Shum, E. Jamieson, M. Bahr, and G. Wallace, “Implicit and explicit memory in children with traumatic brain injury,” J. Clin. Exptl. Neuropsychology, 21, 149–158 (1999).CrossRefGoogle Scholar
  97. 97.
    M. Singh and J. Fulvio, “Bayesian contour extrapolation: Geometric determinations of good continuation,” Vision Res., 47, 783–798 (2007).PubMedCrossRefGoogle Scholar
  98. 98.
    J. G. Snodgrass and E. Hirschman, “Dissociations among implicit and explicit memory tasks: The role of stimulus similarity,” J. Exptl. Psychol. Learn. Mem. Cogn., 20, 150–160 (1994).CrossRefGoogle Scholar
  99. 99.
    J. G. Snodgrass and M. Poster, “Visual-word recognition thresholds for screen-fragmented names of the Snodgrass and Vanderwart pictures,” Behav. Res. Methods, 24, 1–15 (1992).Google Scholar
  100. 100.
    J. G. Snodgrass, B. Smith, K. Feenan, and J. Corwin, “Fragmenting pictures on the Apple Macintosh computer for experimental and clinical applications,” Behav. Res. Methods, 19, 270–274 (1987).Google Scholar
  101. 101.
    H. H. Spitz and M. D. Borland, “Redundancy in line drawings of familiar objects: effects of age and intelligence,” Cogn. Psychol., 2, 196–205 (1971).CrossRefGoogle Scholar
  102. 102.
    H. Stark, Application of Optical Fourier Transforms, Academic Press, New York (1982).Google Scholar
  103. 103.
    R. F. Street, A Gestalt Completion Test, Teachers College, Columbia University, New York (1931).Google Scholar
  104. 104.
    I. I. Tsukkerman and Y. E. Shelepin, “The methods of computer graphics in the neuropsychology,” in: Graphycon-93, St. Petersburg (1993), Vol. 1, pp. 42–53.Google Scholar
  105. 105.
    T. Tversky, W. Geister, and J. Perry, “Contour grouping: closure effects are explained by good continuation and proximity,” Vision Res., 44, 2769–2777 (2004).PubMedCrossRefGoogle Scholar
  106. 106.
    S. Ullman, “Filling-in the gaps: The shape of subjective contours and a model for their generation,” Biol. Cybernet., 25, 1–6 (1976).Google Scholar
  107. 107.
    S. Ullman, “Aligning pictorial descriptions: An approach to object recognition,” Cognition, 32, 193–254 (1989).PubMedCrossRefGoogle Scholar
  108. 108.
    E. Vakil, D. Hoofien, and H. Blachstein, “Total amount learned versus learning rate of verbal and nonverbal information, in differentiating left- from right-brain-injured patients,” Arch. Clin. Neuropsychol., 7, 111–120 (1992).PubMedGoogle Scholar
  109. 109.
    J. R. Vokey, J. G. Baker, G. Hayman, and L. L. Jacoby, “Perceptual identification of visually degraded stimuli,” Behav. Res. Methods, 18, 1–9 (1986).Google Scholar
  110. 110.
    B. A. Wandell, Foundations of Vision, Sinauer Associates, Sunderland, MA (1995).Google Scholar
  111. 111.
    K. E. Warrington, “Neuropsychological studies of object identification,” Phil. Trans. Roy. Soc. Lond., 298, 15–33 (1982).CrossRefGoogle Scholar
  112. 112.
    E. Warrington and M. James, “Disorders of visual perception in patients with localized cerebral lesions,” Neuropsychologia, 5, 253–266 (1967).CrossRefGoogle Scholar
  113. 113.
    E. Warrington and L. Weiskrantz, “New method of testing long-term retention with special reference to amnesic patients,” Nature, 217, 972–974 (1968).PubMedCrossRefGoogle Scholar
  114. 114.
    M. Wertheimer, Laws of Organization in Perceptual Forms, Harcourt Brace Co., London (1938), pp. 71–88.Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2009

Authors and Affiliations

  • Yu. E. Shelepin
    • 1
  • V. N. Chikhman
    • 2
  • N. Foreman
    • 3
  1. 1.Visual Physiology LaboratorySt. PeterburgRussia
  2. 2.Information Technology Sector, I. P. Pavlov Institute of PhysiologyRussian Academy of SciencesSt. PetersburgRussia
  3. 3.Faculty of PsychologyMiddlesex UniversityLondonUK

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