The coefficient of variation (CV), also known as relative standard deviation, has been used to measure the constancy of the Weber fraction, a key signature of efficient neural coding in time perception. It has long been debated whether or not duration judgments follow Weber's law, with arguments based on examinations of the CV. However, what has been largely ignored in this debate is that the observed CVs may be modulated by temporal context and decision uncertainty, thus questioning conclusions based on this measure. Here, we used a temporal reproduction paradigm to examine the variation of the CV with two types of temporal context: full-range mixed vs. sub-range blocked intervals, separately for intervals presented in the visual and auditory modalities. We found a strong contextual modulation of both interval-duration reproductions and the observed CVs. We then applied a two-stage Bayesian model to predict those variations. Without assuming a violation of the constancy of the Weber fraction, our model successfully predicted the central-tendency effect and the variation in the CV. Our findings and modeling results indicate that both the accuracy and precision of our timing behavior are highly dependent on the temporal context and decision uncertainty. And, critically, they advise caution with using variations of the CV to reject the constancy of the Weber fraction of duration estimation.

变异系数（CV），也称为相对标准偏差，已用于测量Weber分数的恒定性，Weber分数是时间感知中有效神经编码的关键特征。持续时间的判决是否遵循韦伯定律一直存在争议，争论的基础是对简历的审查。但是，在本次辩论中被很大程度上忽略的是，所观察到的CV可能会受到时间背景和决策不确定性的影响，因此质疑基于此测度的结论。在这里，我们使用时间再现范例来检查CV在两种类型的时间上下文中的变化：全范围混合与子范围受阻间隔，分别针对视觉和听觉模态中呈现的间隔。我们发现间隔持续时间复制和观察到的CV都有很强的上下文调制能力。然后，我们应用了两阶段贝叶斯模型来预测那些变化。在不假设违反Weber分数恒定性的前提下，我们的模型成功预测了中央趋向效应和CV的变化。我们的发现和建模结果表明，我们计时行为的准确性和精确性都高度依赖于时间上下文和决策不确定性。而且，至关重要的是，他们建议使用CV的变化来拒绝持续时间估计的Weber分数的恒定性。我们的模型成功预测了中央趋向效应和CV的变化。我们的发现和建模结果表明，我们计时行为的准确性和精确性都高度依赖于时间上下文和决策不确定性。而且，至关重要的是，他们建议使用CV的变化来拒绝持续时间估计的Weber分数的恒定性。我们的模型成功预测了中央趋向效应和CV的变化。我们的发现和建模结果表明，我们计时行为的准确性和精确性都高度依赖于时间上下文和决策不确定性。而且，至关重要的是，他们建议使用CV的变化来拒绝持续时间估计的Weber分数的恒定性。