A measure of internal noise based on sample discrimination

Abstract

Internal noise is often inferred from the difference between observed performance and optimum performance in detection and discrimination tasks. It can be measured directly in some cases by observing the extent to which a change in external variability impacts performance. In the studies reported here, external variability was added to an intensity discrimination task by adding a Gaussian random variable with zero mean to the overall level presented in each interval of a two-interval forced-choice task. The standard deviation of the random variable was set to half the mean difference between the levels in the two intervals, resulting in d'(ideal) = 2. As the mean difference and the corresponding standard deviation of the random variable decreased in size, performance was increasingly limited by internal noise, permitting a reliable estimate of internal noise to be obtained. This can be viewed as a sample discrimination task, with one component per sample. In the first study, performance was measured using 2-kHz tones presented at an average level of 70 dB SPL, with mean differences between distributions ranging from 0.1 to 2.2 dB in steps of 0.3 dB. The distributions were either Gaussian in level or in power. Conditions with no external variability were used to obtain a psychometric function. In the second study, performance was measured using 2-kHz tones presented at average levels of 50 and 90 dB SPL, with mean differences ranging from 0.4 to 2.2 dB in steps of 0.6 dB. In both studies, the measure of internal noise was highly reliable and in good agreement with the intensity difference limen (DL) estimated from the psychometric function. Analyses suggest that this measure could be used to estimate the mean difference between the decision distributions as well as the amount of internal noise in cases where the mean difference between the distributions is unknown.