On the normative justification for information gain in Wason's selection task

M. Oaksford and N. Chater (1994, 1996) presented a rational analysis of Wason's selection task in which human performance was argued to be optimal when contrasted with the normative yardstick of Bayesian statistics rather than formal logic. In the present article, it is shown that selecting data according to expected information gain, as proposed by Oaksford and Chater, leads to suboptimal performance in Bayesian hypothesis testing. Procedures are presented that are better justified normatively, their psychological implications are explored, and a number of novel predictions are derived under the sequential as well as the more adequate nonsequential interpretation of the task.