Sedlmeier P, Gigerenzer G
Department of Psychology, Chemnitz University of Technology, Germany.
J Exp Psychol Gen. 2001 Sep;130(3):380-400. doi: 10.1037//0096-3445.130.3.380.
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.
作者提出并测试了一种教授贝叶斯推理的新方法,而之前的教学研究表明在这方面成效甚微。基于G. 吉仁泽和U. 霍夫拉格(1995)的生态框架,作者编写了一个计算机化的辅导程序,以训练人们构建频率表征(表征训练),而不是将概率代入贝叶斯法则(法则训练)。用自然频率进行贝叶斯计算比用概率更简单,而且有进化方面的原因让我们假设认知算法是为处理自然频率而发展出来的。在两项研究中,作者将表征训练与法则训练进行了比较;评判标准是即时学习效果、对新问题的迁移能力以及长期的时间稳定性。法则训练在迁移能力上与表征训练一样好,但表征训练具有更高的即时学习效果和更强的时间稳定性。