A PATTERN-RECOGNITION ACCOUNT OF DECISION-MAKING

In the domain of pattern recognition, experiments have shown that perc eivers integrate multiple sources of information in an optimal manner. In contrast, other research has been interpreted to mean that decisio n making is nonoptimal. As an example, Tversky and Kahneman (1983) hav e shown that subjects commit a conjunction fallacy because they judge it more likely that a fictitious person named Linda is a bank teller a nd a feminist than just a bank teller. This judgment supposedly violat es probability theory, because the probability of two events can never be greater than the probability of either event alone. The present re search tests the hypothesis that subjects interpret this judgment task as a pattern recognition task. If this hypothesis is correct, subject s' judgments should be described accurately by the fuzzy logical model of perception (FLMP)-a successful model of pattern recognition. In th e first experiment, the Linda task was extended to an expanded factori al design with five vocations and five avocations. The probability rat ings were described well by the FLMP and described poorly by a simple probability model. The second experiment included (1) two fictitious p eople, Linda and Joan, as response alternatives and (2) both ratings a nd categorization judgments. Although the ratings were accurately desc ribed by both the FLMP and an averaging of the sources of information, the categorization judgments were described better by the FLMP. These results reveal important similarities in recognizing patterns and in decision making. Given that the FLMP is an optimal method for combinin g multiple sources of information, the probability judgments appear to be optimal in the same manner as pattern-recognition judgments.