A Computational Model of Wason’s Selection Task

We apply an algorithmic learning model of inquiry to model reasoning carried by experimental subjects in Wason’s Selection Task, that represents reasoning in the task as computation of a decision tree that supervenes on (partial) semantic representations. We argue that the resulting model improves on previous probabilistic (Bayesian) and pragmatic (Relevance theory) models of the task. In particular, it suggests that subjects’ selection could in fact be guided by sophisticated patterns of argumentative reasoning.