A distributional and theoretical analysis of reaction time in the reversal task across adulthood.

A distributional and theoretical analysis of reaction time in the reversal task across adulthood.
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DOI:
10.1080/13803395.2019.1703909
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发表时间:
2020-03
影响因子:
2.2
通讯作者:
Driscoll I
Driscoll I
中科院分区:
心理学4区
文献类型:
--
作者:
Leclaire KN;Osmon DC;Driscoll I

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反转学习评估执行功能的组成部分,对于理解认知随年龄的变化很重要。现有的逆转学习文献在很大程度上评估了准确性的衡量标准,但反应时间(RT)尚未得到很好的表征。目前的研究通过检查整个RT分布的分布和理论方面以及准确性来为文献做出贡献。参与者样本包括青年(N=43)和社区居住的健康中年人(N=139)。结果表明,正态-3混合分布最适合样本整体,前高斯分布通过目测检验。年龄与RT的各种测量指标显著相关(p‘s<0.5);年龄越大,有效率和总体RT越高。在广义自适应弹性网络回归中,RT解释了与年龄相关的表现差异,而准确性没有贡献。具体地说,中年人在有效反应时速度较慢,个体内变异性增加,这之前曾被认为与额叶过程较差和年龄相关的认知能力下降有关。总体而言,这些发现强调了检查整个RT分布和测量RT作为一个细分结构以进一步解释反转学习中与年龄相关的差异的重要性。
Reversal learning assesses components of executive function important for understanding cognitive changes with age. Extant reversal learning literature has largely assessed measures of accuracy, but reaction time (RT) has not yet been well characterized. The current study contributes to the literature by examining distributional and theoretical aspects of the entire RT distribution in addition to accuracy. Participant sample included young (N=43) and community dwelling, healthy, middle-aged (N=139) adults. Results showed a Normal-3 Mixture distribution best fit the sample as a whole, with the ex-Gaussian distribution passing visual inspection. Age related significantly to various measures of RT (p’s<0.5); older age was associated with higher both efficient and overall RT. In a generalized adaptive elastic net regression, RT explained age-related differences in performance while accuracy did not contribute. Specifically, middle-aged adults were slower in efficient RT and had increased intra-individual variability which has been previously linked to poorer frontal lobe processes and age-related cognitive decline. Overall, these findings highlight the importance of examining the entire RT distribution and measuring RT as a fractionated construct to further explain age-related differences in reversal learning.
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