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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: [email protected]
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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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