ABSTRACT
A decision rule specifies how information from a presented stimulus situation may be used to determine a course of action, or response. This paper discusses decision rules that are optimal from the standpoint of both a minimal-error criterion and a maximized-expected-value objective. In the first three sections of the paper, the basic decision problem is outlined and well-known results from the yes-no and two-alternative forced-choice paradigms are reviewed. In subsequent sections, the same basic techniques of analysis used in the earlier sections are applied to the “same-different” and “ABX” discrimination tasks to obtain new results. For important special cases of these two discrimination tasks, the optimal decision strategy involves an implicit classification or “categorization” process on the part of the observer. While such a decision strategy differs in a fundamental way from one in which the observer compares differences in sensations (“Sensory Difference Theory”), the predictions of averaged percentage correct scores by the optimal decision strategy and by Sensory Difference Theory are nevertheless often quite similar — for example, for data from a task involving the discrimination of tones differing in frequency, the two sets of predictions are within several percentage points of each other, and each set provides at least a first-order approximation to the obtained performance. The discussion section of the paper considers implications of these results and shows how the optimal decision rule analysis can be applied to a variety of other psychophysical paradigms.
