Feature cues in probability learning: Data base information and judgment

Abstract

Judgments of probabilistic events are often based partly on some information about past similar events. This study investigates the impact of summarized historical data termed a feature cue on performance in a cue probability learning task. Judges (n = 64) made 150 predictions of a criterion variable (Ye) from a single cue variable (X). The feature cue variable (Z) provided judges with the “average past criterion” for the cue value on trial i, i.e., the conditional mean Z = ȲeXi. Availability of the feature cue was varied with an AB-BA transfer design. Results demonstrate that the presence of the feature cue greatly imporved prediction achievement and accuracy. Under certain conditions, consistency and cue weighting were also improved by the feature cue aid. Although the feature cue value itself was not used as a prediction, it served as an anchor, around which judgments were dispersed. Implications for decision making with data base information are discussed.

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