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A novel, unbiased approach to evaluating subsequent search misses in dual target visual search.
Attention, Perception, & Psychophysics ( IF 1.7 ) Pub Date : 2020-07-08 , DOI: 10.3758/s13414-020-02085-0
Mark W Becker 1 , Kaitlyn Anderson 1 , Jan W Brascamp 1
Affiliation  

Research in radiology and visual cognition suggest that finding one target during visual search may result in increased misses for a second target, an effect known as subsequent search misses (SSM). Here, we demonstrate that the common method of calculating second-target detection performance is biased and could produce spurious SSM effects. We describe the source of that bias and document factors that influence its magnitude. We use a modification of signal-detection theory to develop a novel, unbiased method of calculating the expected value for dual-target performance under the null hypothesis. We then apply our novel method to two of our data sets that showed modest SSM effects when calculated in the traditional manner. Our correction reduced the effect size to the point that there was no longer a significant SSM effect. We then applied our method to a published data set that had a larger effect size when calculated using the traditional calculation as well as when using an alternative calculation that was recently proposed to account for biases in the traditional method. We find that both the traditional method and the recently proposed alternative substantially overestimate the magnitude of the SSM effect in these data, but a significant SSM effect persisted even with our calculation. We recommend that future SSM studies use our method to ensure accurate effect-size estimates, and suggest that the method be applied to reanalyze published results, particularly those with small effect sizes, to rule out the possibility that they were spurious.



中文翻译:

一种新颖,公正的方法来评估双目标视觉搜索中的后续搜索缺失。

放射学和视觉认知方面的研究表明,在视觉搜索过程中找到一个目标可能会导致第二个目标的遗漏增加,这种效应被称为后续搜索遗漏(SSM)。在这里,我们证明了计算第二目标检测性能的常用方法是有偏见的,并且可能会产生伪造的SSM效应。我们描述了这种偏差的根源,并记录了影响其大小的因素。我们使用信号检测理论的一种改进,来开发一种新颖的,无偏的方法来计算零假设下双目标性能的期望值。然后,我们将新颖的方法应用于两个以传统方式计算时显示适度SSM效果的数据集。我们的校正将效果大小减小到不再具有明显的SSM效果的程度。然后,我们将我们的方法应用于已发布的数据集,该数据集在使用传统计算以及最近提出的用于考虑传统方法偏差的替代计算时具有较大的影响量。我们发现传统方法和最近提出的替代方法都大大高估了这些数据中SSM效应的幅度,但是即使进行我们的计算,仍然存在显着的SSM效应。我们建议未来的SSM研究使用我们的方法来确保准确的效应量估计,并建议将该方法用于重新分析已发布的结果(尤其是效应量小的结果),以排除它们是虚假的可能性。

更新日期:2020-07-08
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