感觉运动变异性的时间结构:稳定的特质,但有什么用?

Marlou Nadine Perquin, Marieke K van Vugt, Craig Hedge, Aline Bompas
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摘要

人类的表现会随着时间的推移而产生巨大的内生变异,这种变异是个体差异的有力标志。心理学家越来越感兴趣的是,人们意识到变异性并非完全随机,而是经常表现出时间依赖性。然而,对它们的测量和解释却存在一些争议。此外,它们对研究健康和临床人群个体差异的潜在益处仍不明确。在此,我们收集了526名参与者的11项感觉运动和认知任务的新数据集和档案数据集,以研究时间结构的个体差异。我们首先研究了最常见的时间结构测量的个体内可重复性,以测试它们捕捉稳定个体差异的潜力。其次,我们利用以下数据研究这些测量指标的个体间差异:(1) 来自相同数据的任务表现评估;(2) 来自整个任务过程中偶尔出现的思维探究的开动性元认知评级;(3) 自我评估的注意力缺陷相关特征。在所有数据集中,滞后期 1 的自相关性和功率谱密度斜率显示出个体内部在不同阶段的高重复性,并与任务表现相关。去趋势波动分析斜率显示了相同的模式,但可靠性较低。ARFIMA(1,d,1)模型的长期分量(d)显示出较低的可重复性,并且与成绩没有相关性。总体而言,当这些测量指标与参与者的平均主观注意状态或自我评估特征相关时,均未能显示出外部有效性。因此,某些序列依赖性的测量方法可能是稳定的个体特质,但它们在捕捉通常与成绩变异相关的其他构造的个体差异方面的作用似乎有限。最后,我们向研究人员提出了全面的建议:在线版本包含补充材料,可查阅 10.1007/s42113-022-00162-1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Temporal Structure in Sensorimotor Variability: A Stable Trait, But What For?

Human performance shows substantial endogenous variability over time, and this variability is a robust marker of individual differences. Of growing interest to psychologists is the realisation that variability is not fully random, but often exhibits temporal dependencies. However, their measurement and interpretation come with several controversies. Furthermore, their potential benefit for studying individual differences in healthy and clinical populations remains unclear. Here, we gather new and archival datasets featuring 11 sensorimotor and cognitive tasks across 526 participants, to examine individual differences in temporal structures. We first investigate intra-individual repeatability of the most common measures of temporal structures - to test their potential for capturing stable individual differences. Secondly, we examine inter-individual differences in these measures using: (1) task performance assessed from the same data, (2) meta-cognitive ratings of on-taskness from thought probes occasionally presented throughout the task, and (3) self-assessed attention-deficit related traits. Across all datasets, autocorrelation at lag 1 and Power Spectra Density slope showed high intra-individual repeatability across sessions and correlated with task performance. The Detrended Fluctuation Analysis slope showed the same pattern, but less reliably. The long-term component (d) of the ARFIMA(1,d,1) model showed poor repeatability and no correlation to performance. Overall, these measures failed to show external validity when correlated with either mean subjective attentional state or self-assessed traits between participants. Thus, some measures of serial dependencies may be stable individual traits, but their usefulness in capturing individual differences in other constructs typically associated with variability in performance seems limited. We conclude with comprehensive recommendations for researchers.

Supplementary information: The online version contains supplementary material available at 10.1007/s42113-022-00162-1.

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