PDF(Pubmed)
Abstract：
BACKGROUND: Digital neuropsychological tools for diagnosing neurodegenerative diseases in the older population are becoming more relevant and widely adopted because of their diagnostic capabilities. In this context, explicit memory is mainly examined. The assessment of implicit memory occurs to a lesser extent. A common measure for this assessment is the serial reaction time task (SRTT).
OBJECTIVE: This study aims to develop and empirically test a digital tablet-based SRTT in older participants with cognitive impairment (CoI) and healthy control (HC) participants. On the basis of the parameters of response accuracy, reaction time, and learning curve, we measure implicit learning and compare the HC and CoI groups.
METHODS: A total of 45 individuals (n=27, 60% HCs and n=18, 40% participants with CoI-diagnosed by an interdisciplinary team) completed a tablet-based SRTT. They were presented with 4 blocks of stimuli in sequence and a fifth block that consisted of stimuli appearing in random order. Statistical and machine learning modeling approaches were used to investigate how healthy individuals and individuals with CoI differed in their task performance and implicit learning.
RESULTS: Linear mixed-effects models showed that individuals with CoI had significantly higher error rates (b=-3.64, SE 0.86; z=-4.25; P<.001); higher reaction times (F1,41=22.32; P<.001); and lower implicit learning, measured via the response increase between sequence blocks and the random block (β=-0.34; SE 0.12; t=-2.81; P=.007). Furthermore, machine learning models based on these findings were able to reliably and accurately predict whether an individual was in the HC or CoI group, with an average prediction accuracy of 77.13% (95% CI 74.67%-81.33%).
CONCLUSIONS: Our results showed that the HC and CoI groups differed substantially in their performance in the SRTT. This highlights the promising potential of implicit learning paradigms in the detection of CoI. The short testing paradigm based on these results is easy to use in clinical practice.
OBJECTIVE: This study aims to develop and empirically test a digital tablet-based SRTT in older participants with cognitive impairment (CoI) and healthy control (HC) participants. On the basis of the parameters of response accuracy, reaction time, and learning curve, we measure implicit learning and compare the HC and CoI groups.
METHODS: A total of 45 individuals (n=27, 60% HCs and n=18, 40% participants with CoI-diagnosed by an interdisciplinary team) completed a tablet-based SRTT. They were presented with 4 blocks of stimuli in sequence and a fifth block that consisted of stimuli appearing in random order. Statistical and machine learning modeling approaches were used to investigate how healthy individuals and individuals with CoI differed in their task performance and implicit learning.
RESULTS: Linear mixed-effects models showed that individuals with CoI had significantly higher error rates (b=-3.64, SE 0.86; z=-4.25; P<.001); higher reaction times (F1,41=22.32; P<.001); and lower implicit learning, measured via the response increase between sequence blocks and the random block (β=-0.34; SE 0.12; t=-2.81; P=.007). Furthermore, machine learning models based on these findings were able to reliably and accurately predict whether an individual was in the HC or CoI group, with an average prediction accuracy of 77.13% (95% CI 74.67%-81.33%).
CONCLUSIONS: Our results showed that the HC and CoI groups differed substantially in their performance in the SRTT. This highlights the promising potential of implicit learning paradigms in the detection of CoI. The short testing paradigm based on these results is easy to use in clinical practice.
摘要：
背景：用于诊断老年人群神经退行性疾病的数字神经心理学工具由于其诊断能力而变得越来越重要并被广泛采用。在这种情况下，主要检查显式记忆。内隐记忆的评估发生在较小的程度上。此评估的常用措施是串行反应时间任务（SRTT）。
目的：本研究旨在开发并实证检验在患有认知障碍（CoI）和健康控制（HC）的老年参与者中的基于数字平板电脑的SRTT。根据响应精度的参数，反应时间,和学习曲线，我们测量内隐学习并比较HC和CoI组。
方法：共有45名个体（n=27，60％的HCs和n=18，40％的参与者被跨学科团队诊断为CoI）完成了基于片剂的SRTT。他们被依次呈现4个刺激块，第五个块由随机出现的刺激组成。使用统计和机器学习建模方法来研究健康个体和具有CoI的个体在任务表现和内隐学习方面的差异。
结果：线性混合效应模型表明，患有CoI的个体的错误率明显较高（b=-3.64，SE0.86；z=-4.25；P<.001）；反应时间较高（F1,41=22.32；P<.001）；内隐学习较低，通过序列块和随机块之间的响应增加来测量(β=-0.34;SE0.12;t=-2.81;P=.007)。此外，基于这些发现的机器学习模型能够可靠和准确地预测一个人是在HC组还是CoI组，平均预测准确率为77.13%（95%CI为74.67%-81.33%）。
结论：我们的结果表明，HC和CoI组在SRTT中的表现差异很大。这凸显了内隐学习范式在检测CoI方面的潜力。基于这些结果的短测试范例易于在临床实践中使用。
目的：本研究旨在开发并实证检验在患有认知障碍（CoI）和健康控制（HC）的老年参与者中的基于数字平板电脑的SRTT。根据响应精度的参数，反应时间,和学习曲线，我们测量内隐学习并比较HC和CoI组。
方法：共有45名个体（n=27，60％的HCs和n=18，40％的参与者被跨学科团队诊断为CoI）完成了基于片剂的SRTT。他们被依次呈现4个刺激块，第五个块由随机出现的刺激组成。使用统计和机器学习建模方法来研究健康个体和具有CoI的个体在任务表现和内隐学习方面的差异。
结果：线性混合效应模型表明，患有CoI的个体的错误率明显较高（b=-3.64，SE0.86；z=-4.25；P<.001）；反应时间较高（F1,41=22.32；P<.001）；内隐学习较低，通过序列块和随机块之间的响应增加来测量(β=-0.34;SE0.12;t=-2.81;P=.007)。此外，基于这些发现的机器学习模型能够可靠和准确地预测一个人是在HC组还是CoI组，平均预测准确率为77.13%（95%CI为74.67%-81.33%）。
结论：我们的结果表明，HC和CoI组在SRTT中的表现差异很大。这凸显了内隐学习范式在检测CoI方面的潜力。基于这些结果的短测试范例易于在临床实践中使用。
