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The Effect of White-Noise Mask Level on Sinewave Contrast Detection Thresholds and the Critical-Band-Masking Model

Published online by Cambridge University Press:  10 April 2014

Ignacio Serrano-Pedraza  and
Vicente Sierra-Vázquez
Ignacio Serrano-Pedraza
Affiliation:
University of Newcastle
Vicente Sierra-Vázquez*
Affiliation:
Complutense University of Madrid
*
Address correspondence to: Dr. V. Sierra Vázquez, Dpto. de Psicología Básica I. Facultad de Psicología. Universidad Complutense de Madrid, Campus de Somosaguas, 28223 Madrid (Spain). Tel: + 34 91 394 3139. E-mail: vicente@psi.ucm.es
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Abstract

It is known that visual noise added to sinusoidal gratings changes the typical U-shaped threshold curve which becomes flat in log-log scale for frequencies below 10c/deg when gratings are masked with white noise of high power spectral density level. These results have been explained using the critical-band-masking (CBM) model by supposing a visual filter-bank of constant relative bandwidth. However, some psychophysical and biological data support the idea of variable octave bandwidth. The CBM model has been used here to explain the progressive change of threshold curves with the noise mask level and to estimate the bandwidth of visual filters. Bayesian staircases were used in a 2IFC paradigm to measure contrast thresholds of horizontal sinusoidal gratings (0.25-8 c/deg) within a fixed Gaussian window and masked with one-dimensional, static, broadband white noise with each of five power density levels. Raw data showed that the contrast threshold curve progressively shifts upward and flattens out as the mask noise level increases. Theoretical thresholds from the CBM model were fitted simultaneously to the data at all five noise levels using visual filters with log-Gaussian gain functions. If we assume a fixed-channel detection model, the best fit was obtained when the octave bandwidth of visual filters decreases as a function of peak spatial frequency.

El ruido visual añadido a enrejados sinusoidales cambia la típica forma en U de la curva de umbral, que se transforma en una función casi uniforme (en escala log-log) cuando los enrejados son enmascarados por ruido blanco cuya densidad espectral de potencia (o nivel) es alta. Ese hecho se ha explicado mediante el modelo de enmascaramiento basado en bandas críticas (modelo CBM) suponiendo que la anchura de banda relativa (en octavas) de los filtros visuales es constante. Sin embargo, estudios biológicos y psicofísicos apoyan la idea de la variación de la anchura de banda con la frecuencia de sintonía de los filtros. En este trabajo se ha utilizado el modelo CBM para explicar el cambio progresivo de la curva de umbral con el nivel del ruido y, a la vez, para estimar la anchura de banda de los filtros visuales. Para ello, se midieron (utilizando escaleras bayesianas en un paradigma 2IFC) los umbrales de contraste de enrejados sinusoidales (de 0.25 a 8 c/gav), presentados dentro de una ventana Gaussiana fija y enmascarados por ruido blanco 1D estático con cada uno de cinco niveles. Los resultados indican que, en efecto, al aumentar el nivel del ruido, los umbrales de contraste se hacen cada vez mayores y, a la vez, la curva de umbral se va aplanando progresivamente. Utilizando el modelo CBM, los umbrales teóricos se ajustaron a los datos simultáneamente en todos los niveles de ruido suponiendo que la función de ganancia de los filtros visuales es log-Gaussiana y que la detección se lleva a cabo por el filtro sintonizado a la frecuencia del enrejado. Con esos supuestos razonables, el ajuste fue adecuado sólo cuando la anchura de banda relativa de los filtros visuales decrece con su frecuencia espacial de sintonía.

Keywords

contrast detection thresholdspatial white noisecritical-band-masking paradigmumbral de contrasteruido blancomodelo de enmascaramiento
Type
Monographic Section: Spatial Vision and Visual Space
Information
The Spanish Journal of Psychology , Volume 9 , Issue 2 , November 2006 , pp. 249 - 262
Copyright
Copyright © Cambridge University Press 2006

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References

Alcalá-Quintana, R., & García-Pérez, M. A. (2004). The role of parametric assumptions in adaptive Bayesian estimation. Psychological Methods, 9, 250–271.CrossRefGoogle ScholarPubMed
Barten, P.G.J. (1999). Contrast sensitivity of the human eye and its effects on image quality. Bellingham, Washington: SPIE Optical Engineering Press.CrossRefGoogle Scholar
Blackwell, K.T. (1998). The effect of white and filtered noise on contrast detection thresholds. Vision Research, 38, 267–280.CrossRefGoogle ScholarPubMed
DePalma, J. J., & Lowry, E.M. (1962). Sine-wave response of the visual system. II. Sine-wave and square-wave sensitivity. Journal of the Optical Society of America, 52, 328–335.CrossRefGoogle Scholar
De Valois, R.L., Albrecht, D.G., & Thorell, L.G. (1982). Spatial frequency selectivity of cells in macaque visual cortex. Vision Research, 22, 545–559.CrossRefGoogle ScholarPubMed
De Valois, R.L., & De Valois, K.K. (1988). Spatial vision. Oxford: Oxford University Press.Google Scholar
Emerson, P.L. (1986). Observations on maximum-likelihood and Bayesian methods of forced-choice sequential threshold estimation. Perception & Psychophysics, 39, 151–153.CrossRefGoogle ScholarPubMed
Fletcher, H. (1940). Auditory patterns. Reviews of Modern Physics, 12, 47–65.CrossRefGoogle Scholar
García-Pérez, M.A. (1998). Forced-choice staircases with fixed steps sizes: Asymptotic and small-sample properties. Vision Research, 38, 1861–1881.CrossRefGoogle ScholarPubMed
García-Pérez, M. A., & Peli, E. (2001). Luminance artifacts of cathode-ray tube displays for vision research. Spatial Vision, 14, 201–215.CrossRefGoogle ScholarPubMed
Green, D.M., & Swets, J.A. (1966). Signal detection theory and psychophysics. Huntington, NY: Krieger.Google Scholar
Hartmann, W.M. (1998). Signals, sound, and sensation. NY: Springer-Verlag.CrossRefGoogle Scholar
Henning, G.B., Hertz, B.G., & Hinton, J.L. (1981). Effects of different hypothetical detection mechanisms on the shape of spatial-frequency filters inferred from masking experiments. I. noise masks. Journal of the Optical Society of America, 71, 574–581.CrossRefGoogle ScholarPubMed
Hess, R.F., & Nordby, K. (1986). Spatial and temporal limits of vision in the achromat. Journal of Physiology, 371, 365–385.CrossRefGoogle ScholarPubMed
Kelly, D.H. (1975). Spatial frequency selectivity in the retina. Vision Research, 15, 665–672.CrossRefGoogle ScholarPubMed
Kelly, D.H., & Burbeck, C.A. (1984). Critical problems in spatial vision. CRC Critical Reviews in Biomedical Engineering, 10, 125–177.Google ScholarPubMed
King-Smith, P.E., Grigsby, S.S., Vingrys, A.J., Benes, S.C., & Supowit, A. (1994). Efficient and unbiased modifications of the QUEST threshold method: Theory, simulations, experimental evaluation and practical implementation. Vision Research, 34, 885–912.CrossRefGoogle ScholarPubMed
Legge, G.E., Kersten, D., & Burgess, A.E. (1987). Contrast discrimination in noise. Journal of the Optical Society of America A, 4, 391–404.CrossRefGoogle ScholarPubMed
Losada, M.A., & Mullen, K.T. (1995). Color and luminance spatial tuning estimated by noise masking in the absence of off-frequency looking. Journal of the Optical Society of America A, 12, 250–260.CrossRefGoogle ScholarPubMed
Madigan, R., & Williams, D. (1987). Maximum-likelihood psychometric procedures in two-alternative forced-choice: Evaluation and recommendations. Perception & Psychophysics, 42, 240–249.CrossRefGoogle ScholarPubMed
Majaj, N.J., Pelli, D.G., Kurshan, P., & Palomares, M. (2002). The role of spatial frequency channels in letter identification. Vision Research, 42, 1165–1184.CrossRefGoogle ScholarPubMed
Moore, B.C.J. (1997). An introduction to the psychology of hearing (4th ed.). New York: Academic Press.Google Scholar
Morrone, M.C., & Burr, D.C. (1988). Feature detection in human vision: A phase-dependent energy model. Proceedings of the Royal Society of London B, 235, 221–245.Google Scholar
Nelder, J.A., & Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7, 308–313.CrossRefGoogle Scholar
Olzak, L.A., & Thomas, J.P. (1986). Seeing spatial patterns. In Boff, K.R., Kaufman, L., & Thomas, J.P. (Eds.), Handbook of perception and human performance. Volume I (pp. 7:1–7:56). New York: Wiley.Google Scholar
Patterson, R.D. (1974). Auditory filter shape. Journal of the Acoustical Society of America, 55, 802–809.CrossRefGoogle ScholarPubMed
Patterson, R.D. (1976). Auditory filter shapes derived with noise stimuli. Journal of the Acoustical Society of America, 59, 640–654.CrossRefGoogle ScholarPubMed
Peli, E., Arend, L., Young, G., & Goldstein, R. (1993). Contrast sensitivity to patch stimuli: Effects of spatial bandwidth and temporal presentation. Spatial Vision, 7, 1–14.Google ScholarPubMed
Pelli, D.G. (1981). Effects of visual noise. Doctoral dissertation. Cambridge University, Cambridge, UK (unpublished).Google Scholar
Pelli, D.G. (1990). The quantum efficiency of vision. In Blakemore, C. (Ed.), Vision: Coding and efficiency (pp. 3–24). Cambridge, UK: Cambridge University Press.Google Scholar
Robson, J.G. (1966). Spatial and temporal contrast-sensitivity functions of the visual system. Journal of the Optical Society of America, 56, 1141–1142.CrossRefGoogle Scholar
Rovamo, J., Franssila, R., & Näsänen, R. (1992). Contrast sensitivity as a function of spatial frequency, viewing distance and eccentricity with and without spatial noise. Vision Research, 32, 631–637.CrossRefGoogle ScholarPubMed
Schofield, A. (1998). Calibration issues in monochrome raster scan displays. Perception and Cognition Tutorial. Birmingham, UK: University of Birmingham.Google Scholar
Schofield, A., & Georgeson, M.A. (1999). Sensitivity to modulations of luminance and contrast in visual white noise: Separate mechanisms with similar behaviour. Vision Research, 39, 2697–2716.CrossRefGoogle ScholarPubMed
Schofield, A., & Georgeson, M.A. (2003). Sensitivity to contrast modulation: The spatial frequency dependence of second-order vision. Vision Research, 43, 243–259.CrossRefGoogle ScholarPubMed
Serrano-Pedraza, I. (2005). Procesos visuales de demodulación espacial. Unpublished doctoral dissertation, Universidad Complutense, Madrid, Spain. Available at www.ucm.es/BUCM/tesis/psi/ucm-t28909.pdf.Google Scholar
Serrano-Pedraza, I., & Sierra-Vázquez, V. (2004). Efecto del ruido binario en la detección de enrejados sinusoidales. Programa y Resúmenes. Madrid: SEPEX (abstract 141).Google Scholar
Serrano-Pedraza, I., & Sierra-Vázquez, V. (2005). The effect of white-noise mask level on sinewave detection thresholds. Perception (Suppl.), 34, 102 (abstract).Google Scholar
Serrano-Pedraza, I., & Sierra-Vázquez, V. (2006). El paradigma de enmascaramiento con ruido visual: simulación del modelo basado en bandas críticas. In Contreras, M.J., Botella, J., Cabestrero, R., & Gil-Gómez, B. (Eds.), Lecturas de psicología experimental (pp. 183–192). Madrid: UNED.Google Scholar
Sierra-Vázquez, V., & Serrano-Pedraza, I. (2006). An algorithm to equate the contrast power of filtered natural images. Manuscript submitted for publication.Google Scholar
Solomon, J.A. (2000). Channel selection with non-white-noise masks. Journal of the Optical Society of America A, 17, 986–993.CrossRefGoogle ScholarPubMed
Solomon, J.A., & Pelli, D.G. (1994). The visual filter mediating letter identification. Nature, 369, 395–397.CrossRefGoogle ScholarPubMed
Stromeyer, C.F. III, & Julesz, B. (1972). Spatial-frequency masking in vision: Critical bands and spread of masking. Journal of the Optical Society of America, 62, 1221–1232.CrossRefGoogle ScholarPubMed
Talgar, C.P., Pelli, D.G., & Carrasco, M. (2004). Cover attention enhances letter identification without affecting channel tuning. Journal of Vision, 2, 22–31.Google Scholar
Thomas, J.P. (1985). Effect of static-noise and grating masks on detection and identification of grating targets. Journal of the Optical Society of America A, 2, 1586–1592.CrossRefGoogle ScholarPubMed
Van Nes, F.L., & Bouman, M.A. (1967). Spatial modulation transfer function of the human eye. Journal of the Optical Society of America, 57, 401–406.CrossRefGoogle Scholar
Watson, A.B., & Pelli, D.G. (1983). QUEST: A Bayesian adaptive psychometric method. Perception and Psychophysics, 33, 113–120.CrossRefGoogle Scholar
Wilson, H.R., McFarlane, D.K., & Phillips, G.C. (1983). Spatial frequency tuning of orientation selective units estimated by oblique masking. Vision Research, 23, 873–882.CrossRefGoogle ScholarPubMed