Cue Discovery in Probabilistic Environments: Uncertainty and Experimentation

It is argued that cue discovery is an important part of the task of learning from experience in probabilistic environments. Cue discovery involves the identification of new valid predictive cues, and their incorporation into the learner's mental model. In contrast, previous work in "multiple-cue probability learning" has focused principally on learning the quantitative characteristics of given cue-criterion relations. In two studies presented here, 36 college students learned to predict the behavior of a computer-controlled graphic display. The criterion behavior was a function of four to six cues, including several not explicitly identified. Analyses of test results, verbal reports, and behavior during learning indicate that subjects used outcome feedback to accomplish cue discovery. This process was accomplished more effectively when subjects were permitted to experiment by designing their own learning experiences. A distinction is drawn between a system whose behavior is unpredictable because of the presence of unknown controlling variables, and one that contains "random" error. Cue discovery may be more difficult in the latter case, but is accomplished nonetheless.