Probabilistic Mental Models with Continuous Predictors

Gigerenzer and his colleagues have sought to develop psychologically plausible models of human judgment. Their models are classified as ones of bounded rationality based on a principle of one-reason decision making. The models associated with the theory of Probabilistic Mental Models (PMM) have been developed for tasks in which all predictors are binary. This article extends PMM to the case of continuous predictors. The current model employs the limitation on the number of categories people use in making absolute judgments along a single perceptual dimension (7 +/- 2; Miller, 1956). The algorithm transforms each continuous predictor to be consistent with this limitation, then implements a step-down one-reason decision procedure similar to previous PMM models. Like previous PMM models, the 7 +/- 2 model predicts binary judgments as well as a multiple-regression model. However, the model does not successfully predict the probability judgments of individual participants, which is also true of all other models in the literature. Copyright 2000 Academic Press.