Kaye McAulay's Thesis
A Temporal Model of Aural Frequency Discrimination
Submitted for the degree of Doctor of Philosophy in Psychology
Victoria University of Wellington, New Zealand
The importance of temporal information versus place information in frequency
analysis by the ear is a continuing controversy. This dissertation develops a
temporal model which simulates human frequency discrimination. The model gives
quantitative measures of performance for the discrimination of sinusoids in
white gaussian noise. The model simulates human frequency discrimination
performance as a function of frequency and signal-to-noise ratio.
The model's predictions are based on the temporal intervals between the
positive axis crossings of the stimulus. The histograms of these temporal
intervals were used as the underlying distributions from which indices of
discriminability were calculated.
Human frequency discrimination data was obtained for five observers as a
function of frequency and signal-to-noise ratio. The data were analysed using
the method of Group-Operating-Characteristic (GOC) Analysis. This method of
analysis statistically removes unique noise from data. The unique noise was
removed by summing observers' ratings for identical stimuli. This method of
analysis gave human frequency discrimination data with less unique noise than
any existing frequency data. The human data were used for evaluating the model.
The GOC Analysis was also used to study the improvement in d' as a function of
stimulus replications and signal-to-noise ratio.
The model was a good fit to the human data at 250 Hz, for two signal-to-noise
ratios. The model did not fit the data at 1000 Hz or 5000 Hz. There was some
evidence of a transition occuring at 1000 Hz.
This investigation supported the idea that human frequency
discrimination relies on a temporal mechanism at low frequencies with a
transition to some other mechanism at about 1000 Hz.