Department of Psychology, University of Wisconsin-Milwaukee , Milwaukee, WI, USA.
J Clin Exp Neuropsychol. 2020 Nov;42(9):902-913. doi: 10.1080/13803395.2020.1825635. Epub 2020 Oct 18.
Reversal learning is frequently used to assess components of executive function that contribute to understanding age-related cognitive differences. Reaction time (RT) is less characterized in the reversal learning literature, perhaps due to the daunting task of analyzing the entire RT distribution, but has been deemed a generally sensitive measure of cognitive aging. The current study extends our prior work to further characterize distributional properties of the reversal RT distribution and to distinguish groups of individuals with fractionated profiles of performance, which may be of clinical importance within the context of cognitive aging. Participant sample included young ( = 43) and community-dwelling, healthy, middle-aged ( = 139) adults. To explore individual differences, recursive partitioning analysis achieved a high classification rate by specifying decision tree rules that split participants into young and middle-aged groups. Mu (μ, efficient RT) was the most successful parameter in distinguishing age groups while sigma ( and tau ( , ex-Gaussian indices of intra-individual variability) revealed more subtle individual differences. Accuracy measures did not contribute to separating the groups, suggesting that fractionated components of RT, as opposed to accuracy, can distinguish differences between young and middle-aged participants.
反转学习常用于评估导致年龄相关认知差异的执行功能成分。反应时间 (RT) 在反转学习文献中描述较少，可能是由于分析整个 RT 分布的艰巨任务，但它被认为是衡量认知老化的一般敏感指标。本研究扩展了我们之前的工作，以进一步描述反转 RT 分布的分布特性，并区分具有碎片化表现特征的个体群体，这在认知老化的背景下可能具有临床意义。参与者样本包括年轻 ( = 43) 和居住在社区、健康、中年 ( = 139) 成年人。为了探索个体差异，递归分区分析通过指定将参与者分为年轻组和中年组的决策树规则，实现了高分类率。μ (μ，有效 RT) 是区分年龄组的最成功参数，而 σ ( 和 τ ( ，个体内变异性的外高斯指数) 则揭示了更细微的个体差异。准确性测量并没有有助于区分这些组，这表明与准确性相比，RT 的碎片化成分可以区分年轻和中年参与者之间的差异。
