1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Vision Science
  4. Volume 6, 2020
  5. Article

Annual Review of Vision Science

Volume 6, 2020

Image-Computable Ideal Observers for Tasks with Natural Stimuli

Abstract

An ideal observer is a theoretical model observer that performs a specific sensory-perceptual task optimally, making the best possible use of the available information given physical and biological constraints. An image-computable ideal observer (pixels in, estimates out) is a particularly powerful type of ideal observer that explicitly models the flow of visual information from the stimulus-encoding process to the eventual decoding of a sensory-perceptual estimate. Image-computable ideal observer analyses underlie some of the most important results in vision science. However, most of what we know from ideal observers about visual processing and performance derives from relatively simple tasks and relatively simple stimuli. This review describes recent efforts to develop image-computable ideal observers for a range of tasks with natural stimuli and shows how these observers can be used to predict and understand perceptual and neurophysiological performance. The reviewed results establish principled links among models of neural coding, computational methods for dimensionality reduction, and sensory-perceptual performance in tasks with natural stimuli.

Keyword(s): blur, disparity, ideal observer, motion, natural scene statistics, target detection
Loading

Article metrics loading...

/content/journals/10.1146/annurev-vision-030320-041134
2020-09-15
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/vision/6/1/annurev-vision-030320-041134.html?itemId=/content/journals/10.1146/annurev-vision-030320-041134&mimeType=html&fmt=ahah

Literature Cited

  1. Abbey CK, Eckstein MP. 2007. Classification images for simple detection and discrimination tasks in correlated noise. J. Opt. Soc. Am. A 24:12B110–24
     [Google Scholar]
  2. Adelson EH, Bergen JR. 1985. Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2:2284–99
     [Google Scholar]
  3. Albrecht DG, Geisler WS. 1991. Motion selectivity and the contrast-response function of simple cells in the visual cortex. Vis. Neurosci. 7:6531–46
     [Google Scholar]
  4. Anzai A, Ohzawa I, Freeman RD 1999a. Neural mechanisms for encoding binocular disparity: receptive field position versus phase. J. Neurophysiol. 82:2874–90
     [Google Scholar]
  5. Anzai A, Ohzawa I, Freeman RD 1999b. Neural mechanisms for processing binocular information: I. Simple cells. J. Neurophysiol. 82:2891–908
     [Google Scholar]
  6. Artal P, Chen L, Fernández EJ, Singer B, Manzanera S, Williams DR 2004. Neural compensation for the eye's optical aberrations. J. Vis. 4:4281–87
     [Google Scholar]
  7. Badcock DR, Schor CM. 1985. Depth-increment detection function for individual spatial channels. J. Opt. Soc. Am. A 2:71211–15
     [Google Scholar]
  8. Banks MS, Geisler WS, Bennett PJ 1987. The physical limits of grating visibility. Vis. Res. 27:111915–24
     [Google Scholar]
  9. Banks MS, Gepshtein S, Landy MS 2004. Why is spatial stereoresolution so low. J. Neurosci. 24:92077–89
     [Google Scholar]
  10. Bex PJ, Makous W. 2002. Spatial frequency, phase, and the contrast of natural images. J. Opt. Soc. Am. A 19:61096–106
     [Google Scholar]
  11. Bex PJ, Solomon SG, Dakin SC 2009. Contrast sensitivity in natural scenes depends on edge as well as spatial frequency structure. J. Vis. 9:101
     [Google Scholar]
  12. Bishop CM. 2006. Pattern Recognition and Machine Learning Berlin: Springer
  13. Blakemore C. 1970. The range and scope of binocular depth discrimination in man. J. Physiol. 211:3599–622
     [Google Scholar]
  14. Bonnen K, Czuba TB, Whritner JA, Kohn A, Huk AC, Cormack LK 2020. Binocular viewing geometry shapes the neural representation of the dynamic three-dimensional environment. Nat. Neurosci. 23:113–21
     [Google Scholar]
  15. Bradley C, Abrams J, Geisler WS 2014. Retina-V1 model of detectability across the visual field. J. Vis. 14:1222
     [Google Scholar]
  16. Britten KH, Shadlen MN, Newsome WT, Movshon JA 1992. The analysis of visual motion: a comparison of neuronal and psychophysical performance. J. Neurosci. 12:124745–65
     [Google Scholar]
  17. Burg MF, Cadena SA, Denfield GH, Walker EY, Tolias AS et al. 2019. Learning divisive normalization in primary visual cortex. bioRxiv 767285. https://doi.org/10.1101/767285
    [Crossref]
  18. Burge J. 2017. Accurate image-based estimates of focus error in the human eye and in a smartphone camera. J. Soc. Inf. Disp. 1:18–23
     [Google Scholar]
  19. Burge J, Geisler WS. 2011. Optimal defocus estimation in individual natural images. PNAS 108:4016849–54
     [Google Scholar]
  20. Burge J, Geisler WS. 2012. Optimal defocus estimates from individual images for autofocusing a digital camera. Proc. SPIE 8299, Digital Photography VIII art. 82990E Bellingham, WA: SPIE
     [Google Scholar]
  21. Burge J, Geisler WS. 2014. Optimal disparity estimation in natural stereo images. J. Vis. 14:21
     [Google Scholar]
  22. Burge J, Geisler WS. 2015. Optimal speed estimation in natural image movies predicts human performance. Nat. Commun. 6:7900
     [Google Scholar]
  23. Burge J, Jaini P. 2017. Accuracy maximization analysis for sensory-perceptual tasks: computational improvements, filter robustness, and coding advantages for scaled additive noise. PLOS Comput. Biol. 13:2e1005281
     [Google Scholar]
  24. Burge J, McCann BC, Geisler WS 2016. Estimating 3D tilt from local image cues in natural scenes. J. Vis. 16:132
     [Google Scholar]
  25. Burge J, Rodriguez-Lopez V, Dorronsoro C 2019. Monovision and the misperception of motion. Curr. Biol. 29:152586–92.e4
     [Google Scholar]
  26. Burgess AE, Colborne B. 1988. Visual signal detection. IV. Observer inconsistency. J. Opt. Soc. Am. A 5:4617–27
     [Google Scholar]
  27. Burgess AE, Wagner RF, Jennings RJ, Barlow HB 1981. Efficiency of human visual signal discrimination. Science 214:451693–94
     [Google Scholar]
  28. Busse L, Wade AR, Carandini M 2009. Representation of concurrent stimuli by population activity in visual cortex. Neuron 64:6931–42
     [Google Scholar]
  29. Campbell FW, Kulikowski JJ. 1966. Orientational selectivity of the human visual system. J. Physiol. 187:2437–45
     [Google Scholar]
  30. Campbell FW, Westheimer G, Robson JG 1958. Significance of fluctuations of accommodation. J. Opt. Soc. Am. 48:9669
     [Google Scholar]
  31. Carandini M, Heeger DJ. 2012. Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13:151–62
     [Google Scholar]
  32. Carandini M, Heeger DJ, Movshon JA 1997. Linearity and normalization in simple cells of the macaque primary visual cortex. J. Neurosci. 17:218621–44
     [Google Scholar]
  33. Cavanaugh JR, Bair W, Movshon JA 2002. Selectivity and spatial distribution of signals from the receptive field surround in macaque V1 neurons. J. Neurophysiol. 88:52547–56
     [Google Scholar]
  34. Charman WN, Heron G. 1988. Fluctuations in accommodation: a review. Ophthalmic Physiol. Opt. 8:2153–64
     [Google Scholar]
  35. Charman WN, Tucker J. 1978. Accommodation and color. J. Opt. Soc. Am. 68:4459–71
     [Google Scholar]
  36. Chauhan T, Masquelier T, Montlibert A, Cottereau BR 2018. Emergence of binocular disparity selectivity through Hebbian learning. J. Neurosci. 38:449563–78
     [Google Scholar]
  37. Chin BM, Burge J. 2020. Predicting the partition of behavioral variability in speed perception with naturalistic stimuli. J. Neurosci. 40:4864–79
     [Google Scholar]
  38. Cholewiak SA, Love GD, Banks MS 2018. Creating correct blur and its effect on accommodation. J. Vis. 18:91
     [Google Scholar]
  39. Coen-Cagli R, Kohn A, Schwartz O 2015. Flexible gating of contextual influences in natural vision. Nat. Neurosci. 18:111648–55
     [Google Scholar]
  40. Conway BR. 2001. Spatial structure of cone inputs to color cells in alert macaque primary visual cortex (V-1). J. Neurosci. 21:82768–83
     [Google Scholar]
  41. Cormack LK, Czuba TB, Knöll J, Huk AC 2017. Binocular mechanisms of 3D motion processing. Annu. Rev. Vis. Sci. 3:297–318
     [Google Scholar]
  42. Cormack LK, Stevenson SB, Schor CM 1991. Interocular correlation, luminance contrast and cyclopean processing. Vis. Res. 31:122195–207
     [Google Scholar]
  43. Cumming BG, DeAngelis GC. 2001. The physiology of stereopsis. Annu. Rev. Neurosci. 24:203–38
     [Google Scholar]
  44. Czuba TB, Huk AC, Cormack LK, Kohn A 2014. Area MT encodes three-dimensional motion. J. Neurosci. 34:4715522–33
     [Google Scholar]
  45. Czuba TB, Rokers B, Huk AC, Cormack LK 2010. Speed and eccentricity tuning reveal a central role for the velocity-based cue to 3D visual motion. J. Neurophysiol. 104:52886–99
     [Google Scholar]
  46. De Valois RL, Albrecht DG, Thorell LG 1982. Spatial frequency selectivity of cells in macaque visual cortex. Vis. Res. 22:5545–59
     [Google Scholar]
  47. De Vries HL. 1943. The quantum character of light and its bearing upon threshold of vision, the differential sensitivity and visual acuity of the eye. Physica 10:7553–64
     [Google Scholar]
  48. DeAngelis GC, Ohzawa I, Freeman RD 1991. Depth is encoded in the visual cortex by a specialized receptive field structure. Nature 352:6331156–59
     [Google Scholar]
  49. Ernst MO, Banks MS. 2002. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415:6870429–33
     [Google Scholar]
  50. Feord RC, Sumner ME, Pusdekar S, Kalra L, Gonzalez-Bellido PT, Wardill TJ 2020. Cuttlefish use stereopsis to strike at prey. Sci. Adv. 6:2eaay6036
     [Google Scholar]
  51. Field DJ. 1987. Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4:122379–94
     [Google Scholar]
  52. Fincham E. 1951. The accommodation reflex and its stimulus. Br. J. Ophthalmol. 35:7381–93
     [Google Scholar]
  53. Flitcroft DI. 1990. A neural and computational model for the chromatic control of accommodation. Vis. Neurosci. 5:6547–55
     [Google Scholar]
  54. Fox R, Lehmkuhle SW, Bush RC 1977. Stereopsis in the falcon. Science 197:429879–81
     [Google Scholar]
  55. Geisler WS. 1984. Physical limits of acuity and hyperacuity. J. Opt. Soc. Am. A 1:7775–82
     [Google Scholar]
  56. Geisler WS. 1989. Sequential ideal-observer analysis of visual discriminations. Psychol. Rev. 96:2267–314
     [Google Scholar]
  57. Geisler WS. 2003. Ideal observer analysis. The Visual Neuerosciences 10 L Chalupa, J Werner 825–37 Cambridge, MA: MIT Press
     [Google Scholar]
  58. Geisler WS. 2008. Visual perception and the statistical properties of natural scenes. Annu. Rev. Psychol. 59:167–92
     [Google Scholar]
  59. Geisler WS. 2011. Contributions of ideal observer theory to vision research. Vis. Res. 51:7771–81
     [Google Scholar]
  60. Geisler WS, Davila KD. 1985. Ideal discriminators in spatial vision: two-point stimuli. J. Opt. Soc. Am. A 2:91483–97
     [Google Scholar]
  61. Geisler WS, Najemnik J, Ing AD 2009. Optimal stimulus encoders for natural tasks. J. Vis. 9:1317
     [Google Scholar]
  62. Gekas N, Meso AI, Masson GS, Mamassian P 2017. A normalization mechanism for estimating visual motion across speeds and scales. Curr. Biol. 27:101514–20.e3
     [Google Scholar]
  63. Girshick AR, Landy MS, Simoncelli EP 2011. Cardinal rules: visual orientation perception reflects knowledge of environmental statistics. Nat. Neurosci. 14:7926–32
     [Google Scholar]
  64. Goncalves NR, Welchman AE. 2017. “What not” detectors help the brain see in depth. Curr. Biol. 27:101403–8
     [Google Scholar]
  65. Green DM, Swets JA. 1966. Signal Detection Theory and Psychophysics Hoboken, NJ: Wiley
  66. Hansen BC, Essock EA. 2004. A horizontal bias in human visual processing of orientation and its correspondence to the structural components of natural scenes. J. Vis. 4:121044–60
     [Google Scholar]
  67. Harkness L. 1977. Chameleons use accommodation cues to judge distance. Nature 267:346–49
     [Google Scholar]
  68. Harris JM, Watamaniuk SN. 1995. Speed discrimination of motion-in-depth using binocular cues. Vis. Res. 35:7885–96
     [Google Scholar]
  69. Heeger DJ. 1987. Model for the extraction of image flow. J. Opt. Soc. Am. A 4:81455–71
     [Google Scholar]
  70. Heeger DJ. 1992. Normalization of cell responses in cat striate cortex. Vis. Neurosci. 9:2181–97
     [Google Scholar]
  71. Held RT, Cooper EA, Banks MS 2012. Blur and disparity are complementary cues to depth. Curr. Biol. 22:5426–31
     [Google Scholar]
  72. Held RT, Cooper EA, O'Brien JF, Banks MS 2010. Using blur to affect perceived distance and size. ACM Trans. Graph. 29:219
     [Google Scholar]
  73. Hibbard PB. 2008. Binocular energy responses to natural images. Vis. Res. 48:121427–39
     [Google Scholar]
  74. Iyer AV, Burge J. 2018a. Depth variation and stereo processing tasks in natural scenes. J. Vis. 18:64
     [Google Scholar]
  75. Iyer AV, Burge J. 2018b. Optimal binocular disparity estimation in the presence of natural depth variation. J. Vis. 18:10627
     [Google Scholar]
  76. Iyer A, Burge J. 2019. The statistics of how natural images drive the responses of neurons. J. Vis. 19:134
     [Google Scholar]
  77. Jaini P, Burge J. 2017. Linking normative models of natural tasks to descriptive models of neural response. J. Vis. 17:1216
     [Google Scholar]
  78. Jazayeri M, Movshon JA. 2006. Optimal representation of sensory information by neural populations. Nat. Neurosci. 9:5690–96
     [Google Scholar]
  79. Jogan M, Stocker AA. 2015. Signal integration in human visual speed perception. J. Neurosci. 35:259381–90
     [Google Scholar]
  80. Johnson EN, Hawken MJ, Shapley R 2008. The orientation selectivity of color-responsive neurons in macaque V1. J. Neurosci. 28:328096–106
     [Google Scholar]
  81. Julesz B. 1964. Binocular depth perception without familiarity cues. Science 145:3630356–62
     [Google Scholar]
  82. Kane D, Bex P, Dakin S 2011. Quantifying “the aperture problem” for judgments of motion direction in natural scenes. J. Vis. 11:325
     [Google Scholar]
  83. Kim S, Burge J 2018. The lawful imprecision of human surface tilt estimation in natural scenes. eLife 7:31448
     [Google Scholar]
  84. Kim S, Burge J. 2020. Natural scene statistics predict how humans pool information across space in surface tilt estimation. PLOS Comput. Biol. 16:6e1007947
     [Google Scholar]
  85. Knill DC, Richards W. 1996. Perception as Bayesian Inference Cambridge, UK: Cambridge Univ. Press
  86. Kotulak JC, Schor CM. 1986. A computational model of the error detector of human visual accommodation. Biol. Cybern. 54:3189–94
     [Google Scholar]
  87. Kruger PB, Mathews S, Aggarwala KR, Sanchez N 1993. Chromatic aberration and ocular focus: Fincham revisited. Vis. Res. 33:101397–411
     [Google Scholar]
  88. Kruger PB, Mathews S, Katz M, Aggarwala KR, Nowbotsing S 1997. Accommodation without feedback suggests directional signals specify ocular focus. Vis. Res. 37:182511–26
     [Google Scholar]
  89. Landy MS, Banks MS, Knill DC 2011. Ideal-observer models of cue integration. Sensory Cue Integration J Trommershäuser, K Kording, MS Landy 5–29 Oxford, UK: Oxford Univ. Press
     [Google Scholar]
  90. Landy MS, Maloney LT, Johnston EB, Young M 1995. Measurement and modeling of depth cue combination: in defense of weak fusion. Vis. Res. 35:3389–412
     [Google Scholar]
  91. Legge GE, Foley JM. 1980. Contrast masking in human vision. J. Opt. Soc. Am. 70:121458–71
     [Google Scholar]
  92. Legge GE, Kersten D, Burgess AE 1987. Contrast discrimination in noise. J. Opt. Soc. Am. A 4:2391–404
     [Google Scholar]
  93. Liu J, Newsome WT. 2006. Local field potential in cortical area MT: stimulus tuning and behavioral correlations. J. Neurosci. 26:307779–90
     [Google Scholar]
  94. Ma WJ, Beck JM, Latham PE, Pouget A 2006. Bayesian inference with probabilistic population codes. Nat. Neurosci. 9:111432–38
     [Google Scholar]
  95. MacKenzie KJ, Hoffman DM, Watt SJ 2010. Accommodation to multiple-focal-plane displays: implications for improving stereoscopic displays and for accommodation control. J. Vis. 10:822
     [Google Scholar]
  96. Marr D. 1982. Vision New York: W.H. Freeman & Company
  97. Mathews S, Kruger PB. 1994. Spatiotemporal transfer function of human accommodation. Vis. Res. 34:151965–80
     [Google Scholar]
  98. McKee SP, Levi DM, Bowne SF 1990. The imprecision of stereopsis. Vis. Res. 30:111763–79
     [Google Scholar]
  99. Mueller CG. 1951. Frequency of seeing functions for intensity discrimination of various levels of adapting intensity. J. Gen. Physiol. 34:4463–74
     [Google Scholar]
  100. Nagata T, Koyanagi M, Tsukamoto H, Saeki S, Isono K et al. 2012. Depth perception from image defocus in a jumping spider. Science 335:6067469–71
     [Google Scholar]
  101. Najemnik J, Geisler WS. 2005. Optimal eye movement strategies in visual search. Nature 434:7031387–91
     [Google Scholar]
  102. Nishimoto S, Gallant JL. 2011. A three-dimensional spatiotemporal receptive field model explains responses of area MT neurons to naturalistic movies. J. Neurosci. 31:4114551–64
     [Google Scholar]
  103. Nityananda V, Tarawneh G, Rosner R, Nicolas J, Crichton S, Read J 2016. Insect stereopsis demonstrated using a 3D insect cinema. Sci. Rep. 6:18718
     [Google Scholar]
  104. Nitzany EI, Victor JD. 2014. The statistics of local motion signals in naturalistic movies. J. Vis. 14:410
     [Google Scholar]
  105. Nover H, Anderson CH, DeAngelis GC 2005. A logarithmic, scale-invariant representation of speed in macaque middle temporal area accounts for speed discrimination performance. J. Neurosci. 25:4310049–60
     [Google Scholar]
  106. Ogle KN. 1952. On the limits of stereoscopic vision. J. Exp. Psychol. 44:4253–59
     [Google Scholar]
  107. Ohzawa I, DeAngelis GC, Freeman RD 1990. Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. Science 249:49721037–41
     [Google Scholar]
  108. Olshausen BA, Field DJ. 1997. Sparse coding with an overcomplete basis set: a strategy employed by V1. Vis. Res. 37:233311–25
     [Google Scholar]
  109. Owens DA. 1980. A comparison of accommodative responsiveness and contrast sensitivity for sinusoidal gratings. Vis. Res. 20:2159–67
     [Google Scholar]
  110. Parker AJ. 2007. Binocular depth perception and the cerebral cortex. Nat. Rev. Neurosci. 8:5379–91
     [Google Scholar]
  111. Pelli DG. 1990. The quantum efficiency of vision. Vision: Coding and Efficiency C Blackmore 3–24 Cambridge, UK: Cambridge Univ. Press
     [Google Scholar]
  112. Portilla J, Simoncelli EP. 2000. A parametric texture model based on joint statistics of complex wavelet coefficients. Int. J. Comput. Vis. 40:149–71
     [Google Scholar]
  113. Prince SJD, Cumming BG, Parker AJ 2002. Range and mechanism of encoding of horizontal disparity in macaque V1. J. Neurophysiol. 87:1209–21
     [Google Scholar]
  114. Qian N. 1997. Binocular disparity and the perception of depth. Neuron 18:3359–68
     [Google Scholar]
  115. Read JCA, Cumming BG. 2007. Sensors for impossible stimuli may solve the stereo correspondence problem. Nat. Neurosci. 10:101322–28
     [Google Scholar]
  116. Ringach DL. 2002. Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex. J. Neurophysiol. 88:1455–63
     [Google Scholar]
  117. Rokers B, Cormack LK, Huk AC 2008. Strong percepts of motion through depth without strong percepts of position in depth. J. Vis. 8:46
     [Google Scholar]
  118. Rokers B, Cormack LK, Huk AC 2009. Disparity- and velocity-based signals for three-dimensional motion perception in human MT+. Nat. Neurosci. 12:81050–55
     [Google Scholar]
  119. Rose A. 1948. The sensitivity performance of the human eye on an absolute scale. J. Opt. Soc. Am. 38:2196–208
     [Google Scholar]
  120. Rossel S. 1983. Binocular stereopsis in an insect. Nature 302:5911821–22
     [Google Scholar]
  121. Ruff DA, Alberts JJ, Cohen MR 2016. Relating normalization to neuronal populations across cortical areas. J. Neurophysiol. 116:31375–86
     [Google Scholar]
  122. Rust NC, Schwartz O, Movshon JA, Simoncelli EP 2005. Spatiotemporal elements of macaque v1 receptive fields. Neuron 46:6945–56
     [Google Scholar]
  123. Sanada TM, DeAngelis GC. 2014. Neural representation of motion-in-depth in area MT. J. Neurosci. 34:4715508–21
     [Google Scholar]
  124. Schaeffel F, Murphy CJ, Howland HC 1999. Accommodation in the cuttlefish (Sepia officinalis). J. Exp. Biol. 202:3127–34
     [Google Scholar]
  125. Scholl B, Burge J, Priebe NJ 2013. Binocular integration and disparity selectivity in mouse primary visual cortex. J. Neurophysiol. 109:123013–24
     [Google Scholar]
  126. Schrater PR, Knill DC, Simoncelli EP 2001. Perceiving visual expansion without optic flow. Nature 410:6830816–19
     [Google Scholar]
  127. Schumer RA, Julesz B. 1984. Binocular disparity modulation sensitivity to disparities offset from the plane of fixation. Vis. Res. 24:6533–42
     [Google Scholar]
  128. Schütt HH, Wichmann FA. 2017. An image-computable psychophysical spatial vision model. J. Vis. 17:1212
     [Google Scholar]
  129. Sebastian S, Abrams J, Geisler WS 2017. Constrained sampling experiments reveal principles of detection in natural scenes. PNAS 114:28E5731–40
     [Google Scholar]
  130. Sebastian S, Burge J, Geisler WS 2015. Defocus blur discrimination in natural images with natural optics. J. Vis. 15:516
     [Google Scholar]
  131. Sebastian S, Geisler WS. 2018. Decision-variable correlation. J. Vis. 18:43
     [Google Scholar]
  132. Shapley R, Hawken MJ. 2011. Color in the cortex: single- and double-opponent cells. Vis. Res. 51:7701–17
     [Google Scholar]
  133. Shapley R, Nunez V, Gordon J 2019. Cortical double-opponent cells and human color perception. Curr. Opin. Behav. Sci. 30:1–7
     [Google Scholar]
  134. Simoncelli EP, Heeger DJ. 1998. A model of neuronal responses in visual area MT. Vis. Res. 38:5743–61
     [Google Scholar]
  135. Simoncelli EP, Olshausen BA. 2001. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24:1193–216
     [Google Scholar]
  136. Sinha SR, Bialek W, de Ruyter van Steveninck RR 2018. Optimal local estimates of visual motion in a natural environment. arXiv:1812.11878 [q-bio.NC]
  137. Smithline LM. 1974. Accommodative response to blur. J. Opt. Soc. Am. 64:111512–16
     [Google Scholar]
  138. Stevenson SB, Cormack LK, Schor CM, Tyler CW 1992. Disparity tuning in mechanisms of human stereopsis. Vis. Res. 32:91685–94
     [Google Scholar]
  139. Stocker AA, Simoncelli EP. 2006. Noise characteristics and prior expectations in human visual speed perception. Nat. Neurosci. 9:4578–85
     [Google Scholar]
  140. Stromeyer CF, Julesz B. 1972. Spatial-frequency masking in vision: critical bands and spread of masking. J. Opt. Soc. Am. 62:101221–32
     [Google Scholar]
  141. Tanabe S, Haefner RM, Cumming BG 2011. Suppressive mechanisms in monkey V1 help to solve the stereo correspondence problem. J. Neurosci. 31:228295–305
     [Google Scholar]
  142. Thibos LN, Ye M, Zhang X, Bradley A 1992. The chromatic eye: a new reduced-eye model of ocular chromatic aberration in humans. Appl. Opt. 31:193594–600
     [Google Scholar]
  143. Tyler CW, Julesz B. 1978. Binocular cross-correlation in time and space. Vis. Res. 18:1101–5
     [Google Scholar]
  144. van der Willigen RF. 2011. Owls see in stereo much like humans do. J. Vis. 11:710
     [Google Scholar]
  145. Vos JJ, Walraven PL. 1972. An analytical description of the line element in the zone-fluctuation model of color vision: I. Basic concepts. Vis. Res. 12:81327–44
     [Google Scholar]
  146. Wallman J, Winawer J. 2004. Homeostasis of eye growth and the question of myopia. Neuron 43:4447–68
     [Google Scholar]
  147. Walsh G, Charman WN. 1988. Visual sensitivity to temporal change in focus and its relevance to the accommodation response. Vis. Res. 28:111207–21
     [Google Scholar]
  148. Wang B, Ciuffreda KJ. 2005. Foveal blur discrimination of the human eye. Ophthalmic Physiol. Opt. 25:145–51
     [Google Scholar]
  149. Watson AB, Solomon JA. 1997. Model of visual contrast gain control and pattern masking. J. Opt. Soc. Am. A 14:92379–91
     [Google Scholar]
  150. Watt SJ, Akeley K, Ernst MO, Banks MS 2005. Focus cues affect perceived depth. J. Vis. 5:10834–62
     [Google Scholar]
  151. Weiss Y, Simoncelli EP, Adelson EH 2002. Motion illusions as optimal percepts. Nat. Neurosci. 5:6598–604
     [Google Scholar]
  152. Welchman AE. 2016. The human brain in depth: how we see in 3D. Annu. Rev. Vis. Sci. 2:345–76
     [Google Scholar]
  153. Westheimer G. 1979. The spatial sense of the eye. Proctor lecture. Investig. Ophthalmol. Vis. Sci. 18:9893–912
     [Google Scholar]
  154. Westheimer G. 1982. The spatial grain of the perifoveal visual field. Vis. Res. 22:1157–62
     [Google Scholar]
  155. Wheatstone C. 1838. On some remarkable, and hitherto unobserved, phenomena of binocular vision. Philos. Trans. R. Soc. Lond. 128:371–94
     [Google Scholar]
  156. White D, Burge J. 2018. Human binocular disparity estimation with natural stereo-images. J. Vis. 18:10993
     [Google Scholar]
  157. Wildsoet CF, Wong R. 1999. A far-sighted view of myopia. Nat. Med. 5:8879–80
     [Google Scholar]
  158. Zannoli M, Love GD, Narain R, Banks MS 2016. Blur and the perception of depth at occlusions. J. Vis. 16:617
     [Google Scholar]
/content/journals/10.1146/annurev-vision-030320-041134
Loading
Image-Computable Ideal Observers for Tasks with Natural Stimuli
Annual Review of Vision Science 6, 491 (2020); https://doi.org/10.1146/annurev-vision-030320-041134
/content/journals/10.1146/annurev-vision-030320-041134
/content/journals/10.1146/annurev-vision-030320-041134
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error