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Category effects on stimulus estimation: Shifting and skewed frequency distributions-A reexamination

机译:类别对刺激估计的影响:转移和偏移频率分布 - 重新审查

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Duffy, Huttenlocher, Hedges, and Crawford (Psychonomic Bulletin & Review, 17(2), 224-230, 2010) report on experiments where participants estimate the lengths of lines. These studies were designed to test the category adjustment model (CAM), a Bayesian model of judgments. The authors report that their analysis provides evidence consistent with CAM: that there is a bias toward the running mean and not recent stimuli. We reexamine their data. First, we attempt to replicate their analysis, and we obtain different results. Second, we conduct a different statistical analysis. We find significant recency effects, and we identify several specifications where the running mean is not significantly related to judgment. Third, we conduct tests of auxiliary predictions of CAM. We do not find evidence that the bias toward the mean increases with exposure to the distribution. We also do not find that responses longer than the maximum of the distribution or shorter than the minimum become less likely with greater exposure to the distribution. Fourth, we produce a simulated dataset that is consistent with key features of CAM, and our methods correctly identify it as consistent with CAM. We conclude that the Duffy et al. (2010) dataset is not consistent with CAM. We also discuss how conventions in psychology do not sufficiently reduce the likelihood of these mistakes in future research. We hope that the methods that we employ will be used to evaluate other datasets.
机译:Duffy,Huttenlocher,Hedges和Crawford(精神经教公告和评论,17(2),224-230,2010)关于参与者估计线条长度的实验报告。这些研究旨在测试类别调整模型(CAM),拜耳判断模型。作者报告说,他们的分析提供了与CAM一致的证据:跑步平均值和最近的刺激存在偏见。我们重新审视他们的数据。首先,我们试图复制他们的分析,我们获得了不同的结果。其次,我们进行了不同的统计分析。我们发现了重要的新近效应,并确定了几种规格,其中跑步意味着与判断有显着相关。第三,我们对凸轮的辅助预测进行测试。我们没有发现证据表明偏向平均值随着分布的暴露而增加。我们也没有发现,响应比分布的最大值长或短于最小值变得更少,因为更容易接触分布。第四,我们生产与CAM的关键特征一致的模拟数据集,我们的方法正确地将其与CAM保持一致。我们得出结论,Duffy等人。 (2010)数据集与CAM不一致。我们还讨论了心理学的约定如何在未来的研究中不充分降低这些错误的可能性。我们希望我们采用的方法将用于评估其他数据集。

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