Temporal detection in human vision: Dependence on spatial frequency

Journal of the Optical Society of America A: Optics and Image Science, and Vision

Volumn 16, Issue 11, 1999, Pages 2601-2611

  Fredericksen, R E ^a   Hess, R F ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFACT; DEPTH PERCEPTION; HUMAN; NOISE; PERCEPTION; PHYSIOLOGY; PSYCHOLOGICAL MODEL; TIME PERCEPTION; VISION;

ARTIFACTS; HUMANS; MODELS, PSYCHOLOGICAL; NOISE; PERCEPTUAL MASKING; SPACE PERCEPTION; TIME PERCEPTION; VISUAL PERCEPTION;

EID: 0033227037     PISSN: 10847529     EISSN: 15208532     Source Type: Journal    
DOI: 10.1364/JOSAA.16.002601     Document Type: Article

References (48)

1. R. E. Fredericksen and R. F. Hess, “Estimating multiple temporal mechanisms in human vision, ” Vision Res. 38, 1023-1040 (1998).
2. R. E. Fredericksen and R. F. Hess, “Temporal detection in human vision: Dependence on stimulus energy, ” J. Opt. Soc. Am. A 14, 2557-2569 (1997).
3. I. E. Holliday and K. H. Ruddock, “Two spatio-temporal filters in human vision. 1. Temporal and spatial frequency response characteristics, ” Biol. Cybern. 47, 173-190 (1983).
4. J. A. Roufs and F. J. Blommaert, “Temporal impulse and step responses of the human eye obtained psychophysically by means of a drift-correcting perturbation technique, ” Vision Res. 21, 1203-1221 (1981).
5. B. Breitmeyer, D. Levi, and R. S. Harwerth, “Flicker masking in spatial vision, ” Vision Res. 21, 1377-1385 (1981).
6. G. E. Legge, “Sustained and transient mechanisms in human vision: Temporal and spatial properties, ” Vision Res. 18, 69-81 (1978).
7. U. T. Keesey, “Flicker and pattern detection: A comparison of thresholds, ” J. Opt. Soc. Am. 62, 446-448 (1972).
8. J. J. Kulikowski and D. J. Tolhurst, “Psychophysical evidence for sustained and transient detectors in human vision, ” J. Physiol. (London) 232, 149-162 (1973).
9. A. J. Pantle, “Temporal frequency response characteristic of motion channels measured with three different psychophysical techniques, ” Percept. Psychophys. 24, 285-294 (1978).
10. A. J. Pantle, “Temporal determinants of spatial sine-wave masking, ” Vision Res. 23, 749-757 (1983).
11. A. B. Metha and K. T. Mullen, “Temporal mechanisms underlying flicker detection and identification for red-green and achromatic stimuli, ” J. Opt. Soc. Am. A 13, 1969-1980 (1996).
12. A. B. Metha and K. T. Mullen, “Red-green and achromatic temporal filters: A ratio model predicts contrastdependent speed perception, ” J. Opt. Soc. Am. A 14, 984-996 (1997).
13. S. T. Hammett and A. T. Smith, “Two temporal channels or three? A reevaluation, ” Vision Res. 32, 285-291 (1992).
14. R. F. Hess and R. J. Snowden, “Temporal properties of human visual filters: Number, shapes and spatial covariation, ” Vision Res. 32, 47-59 (1992).
15. R. J. Snowden and R. F. Hess, “Temporal frequency filters in the human peripheral visual field, ” Vision Res. 32, 61-72 (1992).
16. M. B. Mandler and W. Makous, “A three channel model of temporal frequency perception, ” Vision Res. 24, 1881-1887 (1984).
17. R. F. Hess and G. T. Plant, “Temporal frequency discrimination in human vision: Evidence for an additional mechanism in the low spatial and high temporal frequency region, ” Vision Res. 25, 1493-1500 (1985).
18. S. J. Waugh and R. F. Hess, “Suprathreshold temporalfrequency discrimination in the fovea and the periphery, ” J. Opt. Soc. Am. A 11, 1199-1212 (1994).
19. S. R. Lehky, “Temporal properties of visual channels measured by masking, ” J. Opt. Soc. Am. A 2, 1260-1272 (1985).
20. We use the term energy in the general sense of Parseval’s theorem or the integrated, squared amplitude of the signal in either the spatiotemporal or Fourier domain. Contrast energy refers to the integrated, squared contrast.
21. D. G. Albrecht, “Visual cortex neurons in monkey and cat: Effect of contrast on the spatial and temporal phase transfer functions, ” Vis. Neurosci. 12, 1191-1210 (1995).
22. W. S. Geisler and D. G. Albrecht, “Cortical neurons: Isolation of contrast gain control, ” Vision Res. 32, 1409-1410 (1992).
23. G. Sclar, “Expression of ‘retinal’ contrast gain control by neurones of the cat’s lateral geniculate cortex, ” Exp. Brain Res. 66, 589-596 (1987).
24. R. M. Shapley and J. D. Victor, “The effect of contrast on the transfer properties of cat retinal ganglion cells, ” J. Physiol. (London) 285, 275-298 (1978).
25. R. M. Shapley and J. D. Victor, “The contrast gain control of the cat retina, ” Vision Res. 19, 431-434 (1979).
26. R. M. Shapley and J. D. Victor, “How the contrast gain control modifies the frequency responses of cat retinal ganglion cells, ” J. Physiol. (London) 318, 161-179 (1981).
27. It is a common practice in the recent literature to refer to these filters as mechanisms. We will use the term in the appropriate context throughout this paper.
28. J. G. Robson, “Spatial and temporal contrast sensitivity functions of the visual system, ” J. Opt. Soc. Am. 56, 1141-1142 (1966).
29. F. W. Campbell and J. G. Robson, “Application of Fourier analysis to the visibility of gratings, ” J. Physiol. (London) 197, 551-566 (1968).
30. D. H. Kelly, “Visual responses to time-dependent stimuli. I. Amplitude sensitivity measurements, ” J. Opt. Soc. Am. 51, 422-429 (1961).
31. D. H. Kelly, “Diffusion model of linear flicker responses, ” J. Opt. Soc. Am. 59, 1665-1670 (1969).
32. D. H. Kelly, “Theory of flicker and transient responses. I. Uniform fields, ” J. Opt. Soc. Am. 61, 537-546 (1971).
33. We use the term filter sensitivity to refer to the ability of a filter to detect the stimulus (signal). This is distinct from the filter’s gain, because signal detectability is also determined by intrinsic noise and filter efficiency.
34. The intrinsic noise level and the filter gain (peak of the filter modulation transfer function) can be written as a ratio in our model. We can therefore know only the relative values of these two parameters. That is, we know the modulation transfer function to within only a scaling constant.
35. D. J. Field, “Relations between the statistics of natural images and the response properties of cortical cells, ” J. Opt. Soc. Am. A 4, 2379-2394 (1987).
36. W. H. Press, A. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipies in C, 2nd ed. (Cambridge U. Press, Cambridge, UK, 1992).
37. A. B. Watson and D. G. Pelli, “QUEST: A Bayesian adaptive psychometric method, ” Percept. Psychophys. 33, 113-120 (1983).
38. N. A. Macmillan and C. D. Creelman, Detection Theory: A User’s Guide (Cambridge U. Press, Cambridge, UK, 1991).
39. Use of residual error as an acceptance metric for a model depends on whether the required assumptions have been met. See the Appendix A for a brief discussion of this topic.
40. The low-pass ad hoc filter was constructed to have a TMTF falling slope that matched the masking data at low temporal test frequencies. The bandpass ad hoc filters were constructed to have rising and falling TMTF slopes that matched the masking data for high temporal test frequencies.
41. We tested all combinations of one, two, and three ad hoc filters for which at least one filter was a low-pass filter. That is, one low-pass filter alone, or one low-pass filter together with one or two bandpass filters. The low-pass ad hoc filter was constructed to have a TMTF falling slope that matched the masking data at low temporal test frequencies. The bandpass filter was constructed to have rising and falling TMTF slopes that matched the masking data for high temporal test frequencies.
42. H. Akaike, “A new look at statistical model identification, ” IEEE Trans. Autom. Control. 19, 716-723 (1974).
43. M. Forster and E. Sober, “How to tell when simpler, more unified, or less ad hoc theories will provide more accurate predictions, ” Br. J. Philos. Sci. 45, 1-35 (1994).
44. J. Yang and W. Makous, “Spatiotemporal separability in contrast sensitivity, ” Vision Res. 34, 2569-2576 (1994).
45. J. Yang and W. Makous, “Implicit masking constrained by spatial inhomogeneities, ” Vision Res. 37, 1917-1927 (1997).
46. D. G. Pelli, “Effects of visual noise, ” Ph.D. dissertation (Cambridge University, Cambridge, UK, 1981).
47. Our experiments have employed temporally windowed and duration-limited signal and noise patterns. The temporal window shapes and durations were identical in all cases, and we currently do not model the temporal integration aspects of detection. Our definitions for power can therefore be considered, without loss of generality, as representing either (1) peak instantaneous power or (2) average power over the duration of the stimulus presentation.
48. D. Lancaster, Lancaster’s Active Filter Cookbook, 2nd ed. (Biddles, Guildford, 1996).