Estimating sensitivity and bias in a yes/no task.

The estimation of sensitivity and bias from data collected in a yes/no detection-theoretic experiment is complicated by the possibility of proportions of 0 or 1 appearing in the resulting contingency table. Inverse normal transforms of these probabilities result in mathematically intractable infinities. Typically, some transformation of the data must be applied prior to parameter estimation. Several transformations have been reviewed in the literature, in terms of both the bias and the variance of the estimates they produce. We propose three generalized transformations, which contain the two most reported transformations as special cases, and consider their performance in terms of the mean square error of the estimates they produce. Results indicate that the '1/N ' and the adaptive log-linear transformations outperform the others. Guidelines for the application of these transformations are presented.