Teaching Bayesian reasoning in less than two hours.
The authors present and test a new method of teaching Bayesian reasoning, something about which previous teaching studies reported little success. Based on G. Gigerenzer and U. Hoffrage's (1995) ecological framework, the authors wrote a computerized tutorial program to train people to construct frequency representations (representation training) rather than to insert probabilities into Bayes's rule (rule training). Bayesian computations are simpler to perform with natural frequencies than with probabilities, and there are evolutionary reasons for assuming that cognitive algorithms have been developed to deal with natural frequencies. In 2 studies, the authors compared representation training with rule training; the criteria were an immediate learning effect, transfer to new problems, and long-term temporal stability. Rule training was as good in transfer as representation training, but representation training had a higher immediate learning effect and greater temporal stability.
Sedlmeier
P
P
Department of Psychology, Chemnitz University of Technology, Germany. peter.sedlmeier@phil.tu-chemnitz.de
Gigerenzer
G
G
D000465:Algorithms; D001499:Bayes Theorem; D003194:Computer-Assisted Instruction; D006801:Humans; D008432:Mathematical Computing; D011337:Probability Learning; D012984:Software