ABSTRACTBase rate neglect refers to people’s apparent tendency to underweight or even ignore base rate information when estimating posterior probabilities for events, such as the probability that a person with a positive cancer-test outcome actually does have cancer. While many studies have replicated the effect, there has been little variation in the structure of the reasoning problems used in those studies. In particular, most experiments have used extremely low base rates, high hit rates, and low false alarm rates. As a result, it is unclear whether the effect is a general phenomenon in human probabilistic reasoning or an anomaly that applies only to a small subset of reasoning problems. Moreover, previous studies have focused on describing empirical patterns of the effect and not so much on the underlying strategies. Here, we address these limitations by testing participants on a broader problem space and modelling their response at a single-participant level. We find that the empirical patterns that have served as evidence for base-rate neglect generalize to the larger problem space. At the level of individuals, we find evidence for large variability in how sensitive participants are to base rates, but with two distinct groups: those who largely ignore base rates and those who almost perfectly account for it. This heterogeneity is reflected in the cognitive modeling results, which reveal that there is not a single strategy that best captures the data for all participants. The overall best model is a variant of the Bayesian model with too conservative priors, tightly followed by a linear-additive integration model. Surprisingly, we find very little evidence for earlier proposed heuristic models. Altogether, our results suggest that the effect known as “base-rate neglect” generalizes to a large set of reasoning problems, but may need a reinterpretation in terms of the underlying cognitive mechanisms.
BASE-RATE NEGLECT AS A FUNCTION OF BASE RATES IN PROBABILISTIC CONTINGENCY LEARNING
