Effects of frequency and duration on psychometric functions for detection of increments and decrements in sinusoids in noise.

Psychometric functions for detecting increments or decrements in level of sinusoidal pedestals were measured for increment and decrement durations of 5, 10, 20, 50, 100, and 200 ms and for frequencies of 250, 1000, and 4000 Hz. The sinusoids were presented in background noise intended to mask spectral splatter. A three-interval, three-alternative procedure was used. The results indicated that, for increments, the detectability index d' was approximately proportional to delta I/I. For decrements, d' was approximately proportional to delta L. The slopes of the psychometric functions increased (indicating better performance) with increasing frequency for both increments and decrements. For increments, the slopes increased with increasing increment duration up to 200 ms at 250 and 1000 Hz, but at 4000 Hz they increased only up to 50 ms. For decrements, the slopes increased for durations up to 50 ms, and then remained roughly constant, for all frequencies. For a center frequency of 250 Hz, the slopes of the psychometric functions for increment detection increased with duration more rapidly than predicted by a "multiple-looks" hypothesis, i.e., more rapidly than the square root of duration, for durations up to 50 ms. For center frequencies of 1000 and 4000 Hz, the slopes increased less rapidly than predicted by a multiple-looks hypothesis, for durations greater than about 20 ms. The slopes of the psychometric functions for decrement detection increased with decrement duration at a rate slightly greater than the square root of duration, for durations up to 50 ms, at all three frequencies. For greater durations, the increase in slope was less than proportional to the square root of duration. The results were analyzed using a model incorporating a simulated auditory filter, a compressive nonlinearity, a sliding temporal integrator, and a decision device based on a template mechanism. The model took into account the effects of both the external noise and an assumed internal noise. The model was able to account for the major features of the data for both increment and decrement detection.