Predicting Resilience Losses in Dyadic Team Performance.

IF 0.6 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL Nonlinear Dynamics Psychology and Life Sciences Pub Date : 2020-07-01
Yannick Hill, Ruud J R Den Hartigh, Ralf F A Cox, Peter De Jonge, Nico W Van Yperen
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Abstract

In the current study, we applied the dynamical systems approach to obtain novel insights into resilience losses. Dyads (n = 42) performed a lateral rhythmical pointing (Fitts) task. To induce resilience losses and transitions in performance, dyads were exposed to ascending and descending scoring scenarios. To assess changes in the complexity of the dyadic pointing performance, reflecting their resilience, we performed cross-recurrence quantification analyses. Then, we tested for temporal patterns indicating resilience losses. We applied lag 1 autocorrelations to assess critical slowing down and mean squared successive differences (MSSD) to assess critical fluctuations. Although we did not find evidence that scoring scenarios produce performance transitions across individuals, we did observe transitions in each condition. Contrary to the lag 1 autocorrelations, our results suggest that transitions in human performance are signaled by increases in the MSSD. Specifically, both positive and negative performance transitions were accompanied with increased fluctuations in performance. Furthermore, negative performance transitions were accompanied with increased fluctuations of complexity, signaling resilience losses. On the other hand, complexity remained stable for positive performance transitions. Together, these results suggest that combining information of critical fluctuations in performance and complexity can predict both positive and negative transitions in dyadic team performance.

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预测二元团队绩效中的弹性损失。
在当前的研究中,我们应用动力系统方法来获得对弹性损失的新见解。双组(n = 42)执行横向节律指向(Fitts)任务。为了诱导弹性损失和表现的转变,二人组暴露于上升和下降的得分情景。为了评估二元指向性能复杂性的变化,反映它们的弹性,我们进行了交叉递归量化分析。然后,我们测试了表明弹性损失的时间模式。我们应用滞后1自相关来评估临界减速,并应用均方连续差异(MSSD)来评估临界波动。尽管我们没有找到证据表明评分场景会在个体之间产生绩效转变,但我们确实观察到了每种情况下的转变。与滞后自相关相反,我们的研究结果表明,人类表现的转变是由MSSD的增加所标志的。具体而言,积极和消极的业绩转变都伴随着业绩波动的增加。此外,消极的绩效转变伴随着复杂性波动的增加,表明弹性损失。另一方面,对于积极的性能转换,复杂性保持稳定。总之,这些结果表明,结合绩效和复杂性的关键波动信息可以预测二元团队绩效的积极和消极转变。
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来源期刊
CiteScore
1.40
自引率
11.10%
发文量
26
期刊最新文献
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