Monica L. Hawley
H. Steven Colburn
Dept. of Biomed. Eng., Boston Univ., 44 Cummington St., Boston, MA 02215
A mathematical description of a psychometric function with two free parameters is fit to fixed-increment data by a nonlinear gradient search technique that incorporates a weighted least squares algorithm. The statistical confidence in the parameter estimates is considered by comparing results from standard confidence interval analysis with those from joint confidence region analysis. Confidence intervals estimate the variability of each parameter alone, ignoring the interaction between the parameters, whereas the joint confidence region gives the confidence in the joint estimation of both parameters together. In our study, both analyses were applied to fixed-increment data from a variety of tests (interaural time and intensity discrimination and N[sub 0]S[sub (pi)] binaural detection) and subjects for two-interval forced choice experiments. Results show that the parameter estimates are affected primarily by the data collected near the midpoint which is not surprising since the parameters in the model are the slope and translation at the midpoint. However, the joint confidence region analysis shows that the statistical confidence of the parameter estimates are greatly affected by data collected at levels that give performance near chance and near perfect. [Work supported by NIDCD (Grant DC00100).]