In this paper we present a model of three central aspects ofprobabilistic cognition: event prediction, feature formation,and attention allocation. While most models of probabilisticreasoning take a parameter estimation and error minimisationapproach (sometimes referred to as ‘predictive coding’, and of-ten described in terms of Bayesian updating), our model takesa contrasting frequentist hypothesis-testing approach. Thischoice is motivated by a series of recent results suggesting thatpeople’s probabilistic reasoning follows frequentist probabilitytheory. In simulation tests we demonstrate that this frequentistmodel, in which predictive features are formed by a process ofnull hypothesis significance testing, can give a successful ac-count of event prediction and attentional switching behaviour.

Most novel noun-noun combinations are polysemous in that they tend to suggest several possible meanings. A finger cup can be a cup in which fingers are washed, a cup shaped like a finger, a narrow cup and so on. In this paper, we present a new theory of concept combination, the constraint theory, that accounts for the polysemy of noun-noun combinations. Constraint theory, which uses three constraints (of inclusion, plausibility and informativeness) acting over a unitary mechanism that generates candidate interpretations, makes certain predictions about the polysemy of different combinations. In particular, it predicts that combinations involving artifact terms should be more polysemous than those involving natural kinds because the former have functional models that promote multiple interpretations. In a single experiment, this prediction is confirmed along with other predictions about the types of interpretation that tend to be produced.