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Models of discrimination based on statistical decision theory distinguish sensitivity (the ability of an observer to reflect a stimulus-response correspondence denned by the experimenter) from response bias (the tendency to favor 1 response over others). Measures of response bias have received less attention than those of sensitivity. Bias measures are classified here according to 1 characteristics. First, the distributions assumed or implied to underlie the observers decision may be normal, logistic, or rectangular. Second, the bias index may measure criterion location, criterion location relative to sensitivity, or likelihood ratio. Both parametric and nonparametric indexes are classified in this manner. The various bias statistics are compared on pragmatic and theoretical grounds, and it is concluded that criterion location measures have many advantages in empirical work. In a simple but important type of discrimination experiment, a stimulus from one of two classes (S \\ or S2) is presented on each trial, and an observer makes one of two corresponding responses (no or yes). Examples of possible stimulus classes are noise and signal-plus-noise in a detection task; new and old words in a recognition memory experiment; and normal
Macmillan et al. (Tue,) studied this question.