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

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