Published in: Topics in Cognitive Science, Volume 10, Issue 1, 192-208 Abstract “We describe a computational model of two central aspects of people’s probabilistic reasoning: descriptive probability estimation and inferential probability judgment. This model assumes that people’s reasoning follows standard frequentist probability theory, but it is subject to random noise. This random noise has a regressive effect in descriptive probability estimation, moving probability estimates away from normative probabilities and toward the center of the probability scale. This random noise has an anti‐regressive effect in inferential judgement, however. These regressive and anti‐regressive effects explain various reliable and systematic biases seen in people’s… Read More