The psychology of moderate prediction. I. Experience with multiple determination

In the experiments reported here, individuals with experience in a multivariate prediction setting showed considerable moderation of subsequent univariate predictions, compared to those without such experience. We show that such moderation of prediction does not result from an abstract rule of regression to the mean; rather, it can be explained by the named error model. According to this model, missing predictors are treated as an error term, with their unknown values replaced by central tendencies. Experiment 1 demonstrates the phenomenon of moderation following multivariate experience and explores its generalization to novel predictors. Moderation occurs even for a perfectly valid predictor, contrary to normative application of a regression strategy. Experiment 2 shows that the phenomenon depends on lack of correlation among the multivariate predictors. This accords with the named error model, which asserts that if missing predictors are perceived to be correlated with the available predictor, their unknown values are replaced by extreme values rather than by central tendencies. Experiment 3 shows that mere exposure to additional predictors has no effect; experience in which multiple predictors are used to make numerical predictions seems to be necessary in order to obtain subsequent moderation. In Experiment 4, feedback is introduced. Moderation of prediction results even without prior multivariate experience. However, multivariate experience produces the moderation effect much more quickly.