Abstract

Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non-monotonicity, in which adding premises to a category-based argument weakens rather than strength-ens it. Relevance theories explain this phenomenon in terms of people’s sensitivity to the relationships amongst premise items. We show that a Bayesian model of category-based induction taking premise sampling assumptions and category similarity into account complements such theories and yields two important predictions: first, that sensitivity to premise relationships can be violated by inducing a weak sampling as-sumption; and second, that premise monotonicity should be restored as a result. We test these predictions with an experiment that manip-ulates people’s assumptions in this regard, showing that people draw qualitatively different conclusions in each case.