A quantitative model of optimal data selection in Wason's selection task
A quantitative model of optimal data selection in Wason's selection task
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DOI：
10.1080/02724980244000053
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发表时间：
2002-10-01
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影响因子：
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通讯作者：
Hattori, M
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文献类型：
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作者：
Hattori, M
Oaksford和Chater(1994)提出的最优数据选择模型成功地将Wason的选择任务形式化(Wason，1966)。然而，该模型包含了一些有问题的假设，而且作为该任务的模型也不够充分，因为它不能提供对卡片选择频率的定量预测。在本文中，对模型进行了修正，以提供对数据的定量拟合。该模型可以基于选择倾向函数(STF)预测卡片的选择频率，或者反过来，它能够从数据中估计主观概率。首先根据该模型对以往的实验数据进行了重新分析。在实验1中，显示了修正模型的优越性。然而，当前件和后件之间的关系被迫偏离双条件形式时，该模型不被支持。实验二证明了对概率信息的充分重视会影响被试的成绩，介绍了一种用概率策略对被试进行排序的实验方法。在这里，该模型得到了使用概率策略的参与者子组的支持。最后，从适应理性的角度对研究结果进行了讨论。
The optimal data selection model proposed by Oaksford and Chater (1994) successfully formalized Wason's selection task (Wason, 1966). The model, however, involved some questionable assumptions and was also not sufficient as a model of the task because it could not provide quantitative predictions of the card selection frequencies. In this paper, the model was revised to provide quantitative fits to the data. The model can predict the selection frequencies of cards based on a selection tendency function (STF), or conversely, it enables the estimation of subjective probabilities from data. Past experimental data were first re-analysed based on the model. In Experiment 1, the superiority of the revised model was shown. However, when the relationship between antecedent and consequent was forced to deviate from the biconditional form, the model was not supported. In Experiment 2, it was shown that sufficient emphasis on probabilistic information can affect participants' performance, A detailed experimental method to sort participants by probabilistic strategies was introduced. Here, the model was supported by a subgroup of participants who used the probabilistic strategy. Finally, the results were discussed from the viewpoint of adaptive rationality.