Most research on skill acquisition and retention focuses on the individual being tested. Yet sometimes another person is responsible for evaluating the individual’s performance. Here, we study the acquisition and retention of rater skill using data for the Graduate Record Examinations (GRE). Our work is based on the idea that response scoring, like other cognitive skills, will gradually improve with amount of practice, and decline with elapsed time since that practice occurred. These classic findings are the focus of a computational cognitive model called the Predictive Performance Equation (PPE). However, the generalizability of these findings to response scoring and the applicability of PPE to that domain have not yet been demonstrated. To address this issue, we leveraged a naturalistic dataset containing rating performance from over 23,000 sessions. Our analyses provide empirical support for PPE and establish a basis for using a model like PPE to personalize rater training requirements.

关于技能获取和保留的大多数研究都集中在被测者身上。但有时，另一个人负责评估个人的表现。在这里，我们使用研究生成绩考试（GRE）的数据研究评估者技能的获取和保留。我们的工作基于这样一个思想，即反应评分与其他认知技能一样，将随着练习次数的增加而逐渐提高，并且随着该练习发生的时间的流逝而下降。这些经典发现是称为预测性能方程（PPE）的计算认知模型的重点。但是，尚未证明这些发现对响应评分的普遍性以及PPE在该领域的适用性。为了解决这个问题，我们利用了一个自然主义的数据集，其中包含超过23种的评分效果，000节。我们的分析为PPE提供了经验支持，并为使用PPE这样的模型个性化评估者培训要求奠定了基础。