Category effects on stimulus estimation: Shifting and skewed frequency distributions

The category adjustment model (CAM) proposes that estimates of inexactly remembered stimuli are adjusted toward the central value of the category of which the stimuli are members. Adjusting estimates toward the average value of all category instances, properly weighted for memory uncertainty, maximizes the average accuracy of estimates. Thus far, the CAM has been tested only with symmetrical category distributions in which the central stimulus value is also the mean. We report two experiments using asymmetric (skewed) distributions in which there is more than one possible central value: one where the frequency distribution shifts over the course of time, and the other where the frequency distribution is skewed. In both cases, we find that people adjust estimates toward the category’s running mean, which is consistent with the CAM but not with alternative explanations for the adjustment of stimuli toward a category’s central value.