OSA's Digital Library

Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editor: Gregory W. Faris
  • Vol. 4, Iss. 13 — Dec. 2, 2009

Frequency tuning of perceptual templates changes with noise magnitude

Craig K. Abbey and Miguel P. Eckstein  »View Author Affiliations


JOSA A, Vol. 26, Issue 11, pp. B72-B83 (2009)
http://dx.doi.org/10.1364/JOSAA.26.000B72


View Full Text Article

Enhanced HTML    Acrobat PDF (1133 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Classification-image analysis has proven to be a valuable tool for revealing features used to perform visual tasks in noise. We use this methodology to investigate how the magnitude of noise influences detection mechanisms, and more specifically, to examine whether observers use a consistent perceptual template across noise magnitude as is often assumed in models. The experiments consist of 2AFC detection of a Gaussian target profile in white noise with RMS contrast levels ranging from 1.25% to 20%. Target contrast was manipulated to maintain a performance level of approximately 80% correct at each noise level. The estimated classification images are presented along with a spatial frequency analysis that consists of radial averages of the frequency domain. The resulting frequency weights show significant within-subject differences across noise levels, as do sampling efficiencies derived from these frequency weights. At low levels of external noise, the classification images are attenuated at low spatial frequencies, giving rise to a more bandpass appearance. At high noise levels, the spatial frequency weights have much less low-frequency attenuation, making them closer to an ideal matched filter. Our results provide direct evidence against the notion of a single consistent perceptual template mediating detection across different levels of noise.

© 2009 Optical Society of America

OCIS Codes
(330.1880) Vision, color, and visual optics : Detection
(330.4060) Vision, color, and visual optics : Vision modeling
(330.6110) Vision, color, and visual optics : Spatial filtering

History
Original Manuscript: April 23, 2009
Revised Manuscript: July 9, 2009
Manuscript Accepted: July 21, 2009
Published: October 5, 2009

Virtual Issues
Vol. 4, Iss. 13 Virtual Journal for Biomedical Optics

Citation
Craig K. Abbey and Miguel P. Eckstein, "Frequency tuning of perceptual templates changes with noise magnitude," J. Opt. Soc. Am. A 26, B72-B83 (2009)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=josaa-26-11-B72


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. N. S. Nagaraja, “Effect of luminance noise on contrast thresholds,” J. Opt. Soc. Am. 54, 950-955 (1964). [CrossRef]
  2. D. G. Pelli, “Effects of visual noise,” Ph.D. thesis (Cambridge Univ., 1981).
  3. Z. L. Lu and B. A. Dosher, “Characterizing observers using external noise and observer models: assessing internal representations with external noise,” Psychol. Rev. 115, 44-82 (2008). [CrossRef] [PubMed]
  4. A. E. Burgess, R. F. Wagner, R. J. Jennings, and H. B. Barlow, “Efficiency of human visual signal discrimination,” Science 214, 93-94 (1981). [CrossRef] [PubMed]
  5. D. G. Pelli, “Vision: coding and efficiency,” in The Quantum Efficiency of Vision, C.Blakemore, ed. (Cambridge Univ. Press, 1990), pp. 3-24.
  6. G. E. Legge, D. Kersten, and A. E. Burgess, “Contrast discrimination in noise,” J. Opt. Soc. Am. A 4, 391-404 (1987). [CrossRef] [PubMed]
  7. D. G. Pelli and B. Farell, “Why use noise?” J. Opt. Soc. Am. A 16, 647-653 (1999). [CrossRef]
  8. J. A. Solomon and D. G. Pelli, “The visual filter mediating letter identification,” Nature 369, 395-397 (1994). [CrossRef] [PubMed]
  9. N. J. Majaj, D. G. Pelli, P. Kurshan, and M. Palomares, “The role of spatial frequency channels in letter identification,” Vision Res. 42, 1165-1184 (2002). [CrossRef] [PubMed]
  10. I. Oruc, M. S. Landy, and D. G. Pelli, “Noise masking reveals channels for second-order letters,” Vision Res. 46, 1493-1506 (2006). [CrossRef]
  11. A. J. Ahumada, “Classification images from Vernier acuity masked by noise,” Perception 25, 18 (1996). (ECVP Abstract Supplement).
  12. B. L. Beard and A. J. Ahumada, “A technique to extract relevant image features for visual tasks,” Proc. SPIE , 3299, 79-85 (1998). [CrossRef]
  13. C. K. Abbey and M. P. Eckstein, “Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments,” J. Vision 2, 66-78 (2002). [CrossRef]
  14. R. F. Murray, P. J. Bennett, and A. B. Sekuler, “Optimal methods for calculating classification images: weighted sums,” J. Vision 2, 79-104 (2002). [CrossRef]
  15. J. A. Solomon, “Noise reveals visual mechanisms of detection and discrimination,” J. Vision 2, 105-120 (2002). [CrossRef]
  16. P. G. Schyns, L. Bonnar, and F. Gosselin, “Show me the features! Understanding recognition from the use of visual information,” Psychol. Sci. 13, 402-409 (2002). [CrossRef] [PubMed]
  17. F. Gosselin and P. G. Schyns, “Bubbles: a technique to reveal the use of information in recognition tasks,” Vision Res. 41, 2261-2271 (2001). [CrossRef] [PubMed]
  18. J. L. Harris, “Resolving power and decision theory,” J. Opt. Soc. Am. 54, 606-611 (1964). [CrossRef]
  19. G. Revesz, H. L. Kundel, and M. A. Graber, “The influence of structured noise on the detection of radiologic abnormalities,” Invest. Radiol. 9, 479-486 (1974). [CrossRef] [PubMed]
  20. P. F. Judy, R. G. Swensson, and M. Szulc, “Lesion detection and signal-to-noise ratio in CT images,” Med. Phys. 8, 13-23 (1981). [CrossRef] [PubMed]
  21. R. F. Wagner and D. G. Brown, “Unified SNR analysis of medical imaging systems,” Phys. Med. Biol. 30, 489-518 (1985). [CrossRef]
  22. H. H. Barrett, “Objective assessment of image quality: effects of quantum noise and object variability,” J. Opt. Soc. Am. A 7, 1266-1278 (1990). [CrossRef] [PubMed]
  23. H. R. Wilson, “A transducer function for threshold and suprathreshold human vision,” Biol. Cybern. 38, 171-178 (1980). [CrossRef] [PubMed]
  24. J. M. Foley and G. E. Legge, “Contrast detection and near-threshold discrimination in human vision,” Vision Res. 21, 1041-1053 (1981). [CrossRef] [PubMed]
  25. A. E. Burgess and B. Colborne, “Visual signal detection. IV. Observer inconsistency,” J. Opt. Soc. Am. A 5, 617-627 (1988). [CrossRef] [PubMed]
  26. Z. L. Lu and B. A. Dosher, “External noise distinguishes attention mechanisms,” Vision Res. 38, 1183-1198 (1998). [CrossRef] [PubMed]
  27. M. P. Eckstein, A. J. Ahumada, Jr., and A. B. Watson, “Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise,” J. Opt. Soc. Am. A 14, 2406-2419 (1997). [CrossRef]
  28. R. D. Fiete, H. H. Barrett, W. E. Smith, and K. J. Myers, “Hotelling trace criterion and its correlation with human-observer performance,” J. Opt. Soc. Am. A 4, 945-953 (1987). [CrossRef] [PubMed]
  29. H. H. Barrett, J. Yao, J. P. Rolland, and K. J. Myers, “Model observers for assessment of image quality,” Proc. Natl. Acad. Sci. U.S.A. 90, 9758-9765 (1993). [CrossRef] [PubMed]
  30. A. E. Burgess, “Statistically defined backgrounds: performance of a modified nonprewhitening observer model,” J. Opt. Soc. Am. A 11, 1237-1242 (1994). [CrossRef]
  31. C. K. Abbey and F. O. Bochud, “Modeling visual detection tasks in correlated noise with linear model observers,” in Handbook of Medical Imaging, J.Beutel, H.L.Kundel, and R.L.Van Metter, eds. (SPIE Press, 2000), pp. 629-654.
  32. J. M. Gold, R. F. Murray, P. J. Bennett, and A. B. Sekuler, “Deriving behavioural receptive fields for visually completed contours,” Curr. Biol. 10, 663-666 (2000). [CrossRef] [PubMed]
  33. R. F. Murray, P. J. Bennett, and A. B. Sekuler, “Classification images predict absolute efficiency,” J. Vision 5, 139-149 (2005). [CrossRef]
  34. C. K. Abbey and M. P. Eckstein, “Classification images for simple detection and discrimination tasks in correlated noise,” J. Opt. Soc. Am. A 24, B110-B124 (2007). [CrossRef]
  35. D. Q. Nykamp and D. L. Ringach, “Full identification of a linear-nonlinear system via cross-correlation analysis,” J. Vision 2, 1-11 (2002). [CrossRef]
  36. C. F. Stromeyer III and B. Julesz, “Spatial-frequency masking in vision: critical bands and spread of masking,” J. Opt. Soc. Am. 62, 1221-1232 (1972). [CrossRef] [PubMed]
  37. H. R. Wilson, D. K. McFarlane, and G. C. Phillips, “Spatial frequency tuning of orientation selective units estimated by oblique masking,” Vision Res. 23, 873-882 (1983). [CrossRef] [PubMed]
  38. A. Tavassoli, I. Linde, A. C. Bovik, and L. K. Cormack, “Eye movements selective for spatial frequency and orientation during active visual search,” Vision Res. 49, 173-181 (2009). [CrossRef]
  39. C. J. Ludwig, M. P. Eckstein, and B. R. Beutter, “Limited flexibility in the filter underlying saccadic targeting,” Vision Res. 47, 280-288 (2007). [CrossRef]
  40. A. E. Burgess, X. Li, and C. K. Abbey, “Visual signal detectability with two noise components: anomalous masking effects,” J. Opt. Soc. Am. A 14, 2420-2442 (1997). [CrossRef]
  41. J. A. Solomon, “Channel selection with non-white-noise masks,” J. Opt. Soc. Am. A 17, 986-993 (2000). [CrossRef]
  42. Y. Zhang, C. K. Abbey, and M. P. Eckstein, “Adaptive detection mechanisms in globally statistically nonstationary-oriented noise,” J. Opt. Soc. Am. A 23, 1549-1558 (2006). [CrossRef]
  43. A. Burgess and H. Ghandeharian, “Visual signal detection. I. Ability to use phase information,” J. Opt. Soc. Am. A 1, 900-905 (1984). [CrossRef] [PubMed]
  44. A. J. Ahumada, Jr. and A. B. Watson, “Equivalent-noise model for contrast detection and discrimination,” J. Opt. Soc. Am. A 2, 1133-1139 (1985). [CrossRef] [PubMed]
  45. C. K. Abbey and M. P. Eckstein, “Classification images for detection, contrast discrimination, and identification tasks with a common ideal observer,” J. Vision 6, 335-355 (2006). [CrossRef]
  46. Y. Morgenstern, J. H. Elder, and Y. Hou, “Contrast dependence of spatial summation revealed by classification image analysis,” J. Vision 4, 439a (2004). (Vision Science Society Abstract Supplement). [CrossRef]
  47. I. Kurki, A. Hyvärinen, and P. I. Laurinen, “Characterising signal and noise in contrast detection by classification images,” Perception 35 (ECVP Abstract Supplement), 89 (2006).
  48. W. W. Peterson, T. G. Birdsall, and W. C. Fox, “The theory of signal detectability,” Trans. IRE-PGIT 4, 171-212 (1954).
  49. W. P. Tanner and T. G. Birdsall, “Definitions of d′ and η as psychophysical measures,” J. Acoust. Soc. Am. 30, 922-928 (1958). [CrossRef]
  50. D. M. Green and J. A. Swets, Signal Detection Theory and Psychophysics (Peninsula, 1988).
  51. S. A. Klein, “Measuring, estimating, and understanding the psychometric function: a commentary,” Percept. Psychophys. 63, 1421-1455 (2001). [CrossRef]
  52. Z. L. Lu and B. A. Dosher, “Characterizing the spatial-frequency sensitivity of perceptual templates,” J. Opt. Soc. Am. A 18, 2041-2053 (2001). [CrossRef]
  53. A. J. Ahumada, Jr., “Putting the visual system noise back in the picture,” J. Opt. Soc. Am. A 4, 2372-2378 (1987). [CrossRef] [PubMed]
  54. A. J. Ahumada and J. Lovell, “Stimulus features in signal detection,” J. Acoust. Soc. Am. 49, 1751-1756 (1971). [CrossRef]
  55. A. Ahumada, Jr. and R. Marken, “Time and frequency analyses of auditory signal detection,” J. Acoust. Soc. Am. 57, 385-390 (1975). [CrossRef] [PubMed]
  56. C. K. Abbey and M. P. Eckstein, “Optimal shifted estimates of human-observer templates in two-alternative forced-choice experiments,” IEEE Trans. Med. Imaging 21, 429-440 (2002). [CrossRef] [PubMed]
  57. S. Zhang, C. K. Abbey, and M. P. Eckstein, “Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds,” Visual Neurosci. 26, 93-108 (2009). [CrossRef]
  58. 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). [CrossRef] [PubMed]
  59. A. P. Pentland, “Fractal-based description of natural scenes,” IEEE Trans. Pattern Anal. Mach. Intell. 6, 661-673 (1984). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited