The processing of deviant information in prediction and evaluation

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Abstract
Analyses of information integration and of retention were used to examine the processing of deviant information in prediction and evaluation tasks. Sets of test scores were presented serially for a group of hypothetical students, and subjects were asked to evaluate the performance of each student or predict each student’s performance on a comprehensive final exam. An averaging model with greater weight for the more recent scores than for the earlier scores was supported for both types of task, but the recency was more pronounced in the prediction task. Weighting of deviant scores differed in the prediction and evaluation tasks. Significant discounting (underweighting) of deviant scores was obtained only in the prediction task, The ability to recall deviant scores on uncued tests of retention was higher in the prediction task than in the evaluation task. Prediction of future performance based on inconsistent measures of past performance thus appears to be an active process involving the discovery and discounting of unrepresentative information.