In this thesis, we consider the validity of a human probabilistic learning model applied to the prediction of errors associated with the absolute identification of tones. It is shown that the probabilistic learning model describes the human error process adequately.
The model parameters are estimated by three methods which are the method of maximum likelihood, of moments and of least squares. The MLE version of the model has the best predictive power the ME version is more readily obtainable and may be more practical. The LSE version has the poorest predictive power.
