The multiple regression model has been applied to the study of judgment primarily through the paradigms known as policy capturing and multiple cue probability learning (MCPL). The former involves modeling the way the decision maker weights predictive information; the latter focuses on the process by which the decision maker acquires this strategy. In the first part of the paper, the logic underlying the two approaches is examined, and the research generated by each is discussed. Based on this comparative review, it is argued that neither approach has yet lived up to its potential because each has concentrated too narrowly on particular kinds of issues, at least some of which present serious logical difficulties. Policy capturing, for example, has sought to isolate judgment processes through multiple regression despite the fact that it is logically capable of capturing only predictions. For its part, MCPL research has fallen short by limiting itself to variables suggested by Brunswik's lens model. Better use could be made of the model if it were applied in a more functional manner--one in which it is used to index performance rather than to infer cognitive processes. The functional approach serves as a guiding philosophy for a series of experiments that are described in the second part of the paper. The primary question of interest in these experiments was how the format of displaying visual information affects subsequent judgments and decisions. Two types of displays, numerical and graphical, were investigated using the policy capturing paradigm in the first three experiments and MCPL in the fourth one. The most consistent finding was that subjects' cue weighting differed reliably with type of format. Numerical policies tended to be less precise than graphical ones, but accuracy of predictions did not differ with display. The implications of these findings are discussed both from a practical and theoretical perspective.
