Research on input processing and processing instruction has often employed a scoring method known as trials to criterion to observe the effects of instruction that emerge during training. Despite its common use in this research (see Fernández, 2021) this metric has never been evaluated critically. The present study first discusses several challenges associated with trials to criterion, including issues with its conceptual and methodological implementation. The study then introduces three alternative approaches for analyzing accuracy data collected during training sequences: trials to accuracy threshold, growth curve analysis, and bootstrapped differences of timeseries. For each approach, advantages and disadvantages are discussed and example analyses are presented using data from previous research. This discussion shows how these alternative approaches can supplement current trials-to-criterion-based analyses, expand the methodological choices available to researchers, and permit new and interesting research questions.

输入处理和处理指令的研究经常采用一种称为标准试验的评分方法来观察训练过程中出现的指令效果。尽管该指标在本研究中被广泛使用（参见 Fernández，2021），但从未经过严格评估。本研究首先讨论了与标准试验相关的几个挑战，包括其概念和方法实施的问题。然后，该研究介绍了三种用于分析训练序列期间收集的准确性数据的替代方法：准确性阈值试验、增长曲线分析和时间序列的引导差异。对于每种方法，都讨论了优点和缺点，并使用先前研究的数据进行了示例分析。本讨论展示了这些替代方法如何补充当前基于标准的试验分析，扩大研究人员可用的方法选择，并允许提出新的有趣的研究问题。