Computerized cognitive training (CCT) has been found to improve a range of skills such as attention, working memory, inhibition control, and decision making. However, the relationship between the initial performance, amount of improvement, time constant, and asymptotic performance level in CCT is still unclear. In the current study, we performed selective attention training on college students and addressed this issue by mathematically modeling the learning curve with an exponential function. Twenty-nine students completed approximately 10 days of CCT. Presentation time served as the dependent variable and was measured by three-down/one-up adaptive algorithms. We fitted an exponential function to the estimated block thresholds during CCT and obtained three learning parameters (amount of improvement, time constant, and asymptotic performance level) for all subjects. The initial performance was defined by the sum of the amount of improvement and the asymptotic performance level. Pearson correlation analyses were conducted between the initial performance and the three leaning parameters. The initial performance was positively correlated with the amount of improvement and asymptotic performance level, but was negatively correlated with the time constant. The time constant was negatively correlated with the amount of improvement and asymptotic performance level. Poorer initial performance was linked to a larger amount of improvement, shorter time constant, and higher asymptotic threshold, which supported the compensation account. Our results may help improve the present understanding of the nature of the CCT process and demonstrate the advantages of using a customized training protocol to enhance the efficiency of cognitive training in practical applications.

中文翻译：

---

初始表现预测计算机化认知训练的改进：来自选择性注意任务的证据

已发现计算机化认知训练 (CCT) 可以提高一系列技能，例如注意力、工作记忆、抑制控制和决策。然而，CCT 的初始性能、改进量、时间常数和渐近性能水平之间的关系仍不清楚。在当前的研究中，我们对大学生进行了选择性注意力训练，并通过使用指数函数对学习曲线进行数学建模来解决这个问题。29 名学生完成了大约 10 天的 CCT。演示时间作为因变量，由三下/一上自适应算法测量。我们在 CCT 期间对估计的块阈值拟合了一个指数函数，并获得了三个学习参数（改进量、时间常数、和渐近表现水平）所有科目。初始性能由改进量和渐近性能水平的总和定义。在初始性能和三个学习参数之间进行了 Pearson 相关分析。初始性能与改进量和渐近性能水平呈正相关，但与时间常数呈负相关。时间常数与改进量和渐近性能水平呈负相关。较差的初始性能与更大的改进量、更短的时间常数和更高的渐近阈值有关，这支持了补偿帐户。