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拒绝径向频率模式的概率总和,没那么快!

Rejecting probability summation for radial frequency patterns, not so Quick!

作者信息

Baldwin Alex S, Schmidtmann Gunnar, Kingdom Frederick A A, Hess Robert F

机构信息

McGill Vision Research, Department of Ophthalmology, McGill University, Montreal, Canada.

McGill Vision Research, Department of Ophthalmology, McGill University, Montreal, Canada.

出版信息

Vision Res. 2016 May;122:124-134. doi: 10.1016/j.visres.2016.03.003. Epub 2016 May 2.

Abstract

Radial frequency (RF) patterns are used to assess how the visual system processes shape. They are thought to be detected globally. This is supported by studies that have found summation for RF patterns to be greater than what is possible if the parts were being independently detected and performance only then improved with an increasing number of cycles by probability summation between them. However, the model of probability summation employed in these previous studies was based on High Threshold Theory (HTT), rather than Signal Detection Theory (SDT). We conducted rating scale experiments to investigate the receiver operating characteristics. We find these are of the curved form predicted by SDT, rather than the straight lines predicted by HTT. This means that to test probability summation we must use a model based on SDT. We conducted a set of summation experiments finding that thresholds decrease as the number of modulated cycles increases at approximately the same rate as previously found. As this could be consistent with either additive or probability summation, we performed maximum-likelihood fitting of a set of summation models (Matlab code provided in our Supplementary material) and assessed the fits using cross validation. We find we are not able to distinguish whether the responses to the parts of an RF pattern are combined by additive or probability summation, because the predictions are too similar. We present similar results for summation between separate RF patterns, suggesting that the summation process there may be the same as that within a single RF.

摘要

径向频率(RF)模式用于评估视觉系统如何处理形状。人们认为它们是被整体检测到的。这一点得到了一些研究的支持,这些研究发现,对于RF模式的总和大于如果各个部分是被独立检测然后仅通过它们之间的概率总和随着周期数增加而性能才得以改善的情况。然而,这些先前研究中所采用的概率总和模型是基于高阈值理论(HTT),而非信号检测理论(SDT)。我们进行了评级量表实验来研究接收器操作特性。我们发现这些特性呈SDT所预测的曲线形式,而非HTT所预测的直线形式。这意味着为了测试概率总和,我们必须使用基于SDT的模型。我们进行了一组总和实验,发现阈值随着调制周期数的增加而降低,降低速率与之前发现的大致相同。由于这可能与相加总和或概率总和都一致,我们对一组总和模型进行了最大似然拟合(我们的补充材料中提供了Matlab代码)并使用交叉验证评估拟合情况。我们发现我们无法区分对RF模式各部分的响应是通过相加总和还是概率总和进行组合的,因为预测结果过于相似。对于不同RF模式之间的总和,我们也呈现了类似的结果,这表明那里的总和过程可能与单个RF内的总和过程相同。

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