Proceedings of the Annual Meeting of the Cognitive Science Society

Previous research on Bayesian inference, reporting poor performance by students and experts alike, has often led to the conclusion that the mind lacks the appropriate cognitive algorithm. We argue that this conclusion is unjustified because it does not take into account the information format in which this cognitive algorithm is designed to operate. We demonstrate that a Bayesian algorithm is computationally simpler when the information is represented in a frequency rather than a probability format that has been used in previous research. A frequency format corresponds to the way information is acquired in natural sampling—sequentially and without constraints on which observations will be included in the sample. Based on the assumption that performance will reflect computational complexity, we predict that a frequency format yields more Bayesian solutions than a probability format. We tested this prediction in a study conducted with 48 physicians. Using outcome and process analysis, we categorized their individual solutions as Bayesian or non-Bayesian. When information was presented in the frequency format, 46 % of their inferences were obtained by a Bayesian algorithm, as compared to only 10% when the problems were presented in the probability format. We discuss the impact of our results on teaching statistical reasoning.