Dominance rates arising from Temporal Dominance of Sensations (TDS) data are almost always plotted and understood with reference to chance and significance lines, which are based on the assumption of random attribute selection. These lines are auxiliary to the chart, and used to interpret the dominance rates; when the dominance rate for some attribute exceeds the significance line, the conventional interpretation is that this attribute is being noticed significantly. Thus, these auxiliary lines are used to provide a convenient, qualitative interpretation of TDS data. However, the concept of the significance lines has some deficiencies that we explore in this paper. We were interested in what real TDS data look like under a true null hypothesis of random attribute selection. To obtain such data, we sampled from real TDS data (keeping products and assessors' selection patterns intact) to create dominance rates based on random attribute selections. Dominance rates exceed the significance line about 5% of the time (across attributes and time points), as expected. These curves differed from any real TDS curves that we have ever observed previously insofar as that they were unusually flattened. Next, we sampled again from real TDS data (keeping attributes and assessors' selection patterns intact) to create dominance rates based on attribute selections for random (hybrid) products. Derived TDS curves much more closely resemble curves from real TDS studies and show a lot of "significances", even though products are artificially created by the permutations. This is due to properties of the products that are inherent to the product category: e.g., hard will occur early during the evaluation of a chocolate (if at all), but not towards the end. From these simulations, we reach two conclusions. First, we recognize that the hypothesis that assessors select the dominant attribute completely at random is almost always false. Second, the reason that it is false is that, regardless of the product, there is a time-dependent category signature present, as well as competition between attributes, such that significances for one attribute depend on which other attributes are on the list. These properties limit the informative value of the conventional TDS significance lines that are widely used. As an alternative, we explore the possibility of context-dependent significance thresholds which are customized for and aligned with research objectives. These reference lines are recommended for improved interpretation of data arising from TDS studies.

中文翻译：

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在您的TDS研究中，评估者是否仅凭偶然选择了属性，并且知道它有多重要？

由感觉的时间优势（TDS）数据产生的优势率几乎总是根据机会和重要性线绘制和理解的，这些机会和重要性线基于随机属性选择的假设。这些线是图表的辅助，用于解释优势率。当某些属性的优势率超过重要性线时，常规解释是该属性被显着注意到。因此，这些辅助线用于提供对TDS数据的方便，定性的解释。但是，重要性线的概念在本文中有一些不足之处。我们对在随机属性选择的真实零假设下真实的TDS数据是什么样感兴趣。要获取此类数据，我们从真实的TDS数据中采样（保持产品和评估者的选择模式不变），以基于随机属性选择创建优势率。如预期的那样，优势率超过重要性线的时间大约为5％（跨越属性和时间点）。这些曲线与我们以前观察到的任何实际TDS曲线都不同，因为它们异常平整。接下来，我们再次从真实的TDS数据中采样（保持属性和评估者的选择模式不变），以基于随机（混合）产品的属性选择来创建优势率。推导的TDS曲线与实际TDS研究的曲线非常相似，并且显示出许多“意义”，即使产品是由置换人为地创建的。这归因于产品类别固有的产品属性：例如，在巧克力评估过程中（如果有的话）会在早期（而不是最终）出现硬质现象。从这些模拟中，我们得出两个结论。首先，我们认识到评估者完全随机选择主导属性的假设几乎总是错误的。其次，它是错误的原因是，无论产品如何，都存在与时间相关的类别签名，以及属性之间的竞争，因此，一个属性的重要性取决于列表中还有哪些其他属性。这些属性限制了被广泛使用的常规TDS重要性行的信息价值。作为备选，我们探索了为研究目标量身定制并与研究目标相一致的上下文相关重要性阈值的可能性。建议使用这些参考线，以更好地解释TDS研究产生的数据。