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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 17, Iss. 6 — Jun. 1, 2000
  • pp: 986–993

Channel selection with non-white-noise masks

Joshua A. Solomon  »View Author Affiliations


JOSA A, Vol. 17, Issue 6, pp. 986-993 (2000)
http://dx.doi.org/10.1364/JOSAA.17.000986


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Abstract

It is frequently assumed that the accuracy with which luminance gratings can be detected depends solely on the signal-to-noise ratio at the output of a single linear channel. Proportionality between threshold elevation and power spectral density is implicit in this assumption. I demonstrate that this proportionality does not hold for 1-cycle/degree gratings masked by low-pass noise with a 0.5-cycle/degree cutoff frequency. This implies that different channels can mediate detection, depending on the contrast of masking stimuli.

© 2000 Optical Society of America

OCIS Codes
(330.0330) Vision, color, and visual optics : Vision, color, and visual optics
(330.1880) Vision, color, and visual optics : Detection
(330.4060) Vision, color, and visual optics : Vision modeling
(330.5510) Vision, color, and visual optics : Psychophysics

History
Original Manuscript: July 28, 1999
Revised Manuscript: February 23, 2000
Manuscript Accepted: February 23, 2000
Published: June 1, 2000

Citation
Joshua A. Solomon, "Channel selection with non-white-noise masks," J. Opt. Soc. Am. A 17, 986-993 (2000)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-17-6-986


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References

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  18. When the axes in Fig. 7(a) are rotated so that the dashed line (which intersects the regression line where it intersects the mean threshold elevation) becomes the abscissa, the slope of the data points remains significant (p<0.04). For Figs. 7(b) and 7(c) the p values are approximately 0.5 and 0.4, respectively.
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  22. In another section of their paper, Losada and Mullen report absolute thresholds (where N=0, not shown in Fig. 3). These data were fitted simultaneously with the data in Fig. 3. If these data are excluded, the addition of parameters pi still does not improve the fit to subject MAL’s data, but it does reduce the RMS error of the fit to subject MJS’s data by 1%.

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