The extent to which we generalize a novel property from asample of familiar instances to novel instances depends on thesample used. In these experiments, we are interested in twosample characteristics: number of types (discrete entities) andnumber of tokens (copies of the same entity) that share a novelproperty. Existing studies permit separate and conditionalhypotheses about the effects of adding types and tokens, but nostudy has examined the effects of both variables ongeneralization stimuli varying in similarity. We find thatadding types broadens generalization to similar stimuli, buttightens generalization to dissimilar stimuli. Adding tokensdoes not affect generalization, but adding repetitions that areframed as types produces some tightening. Implications formodels of inductive reasoning are discussed.

Two experiments examined theoretical accounts of why people fail to consider alternative hypotheses in judgments under uncertainty. Experiment 1 found that a majority of participants failed to spontaneously search for information about an alternative hypothesis, even when this required minimal effort. This bias was reduced when a specific alternative was mentioned before search. Experiment 2 showed that when participants were given the likelihoods of the data given a focal hypothesis p(D|H) and an alternative hypothesis p(D|¬H), they gave estimates of p(H|D) that were consistent with Bayesian principles. The results show that neglect of the alternative hypothesis typically occurs at the initial stage of problem representation. However judgments are more consistent with Bayesian norms when they involve utilizing information about a given alternative

We outline and test a Bayesian model of the effects of evidencesampling on property induction. Our model assumes thatpeople are sensitive to the effects of different sampling framesapplied to sampled evidence. Two studies tested the model bycomparing property generalization following exposure tosamples selected because they belong to the same taxonomiccategory or because they share a salient property. Both studiesfound that category-based sampling led to broadergeneralization than property-based sampling. In line withmodel predictions, these differences were attenuated when amixture of positive and negative evidence was presented(Experiment 1) and when category-property relations wereprobabilistic rather than deterministic (Experiment 2).

When asked to assess the deductive validity of an argument, people are influenced by their prior knowledge of the content. Recently, two competing explanations for this belief bias effect have been proposed, each based on signal detection theory. Under a response bias explanation, people set more lenient decision criteria for believable than for unbelievable arguments. Alternatively, believable and unbelievable arguments may differ in subjective argument strength for both valid and invalid items. Two experiments tested these accounts by asking participants to assess the validity of categorical syllogisms and rate their confidence. Conclusion- believability was manipulated either within- or between- groups. A two-step signal detection model was applied to examine the effects on the relative location of the decision threshold and the distributions of argument strength. Equivalent belief bias effects were found when believability was manipulated within- and between-groups, supporting the view that the belief bias effect is due to response bias.