Digaeva Aina, Bishop Daniel T, Szameitat Andre J
Department of Life Sciences, Centre for Clinical and Cognitive Neuroscience, Brunel University London, Kingston Lane, Uxbridge, Middlesex, United Kingdom.
PLoS One. 2024 Dec 27;19(12):e0312749. doi: 10.1371/journal.pone.0312749. eCollection 2024.
Multitasking (MT)-performing more than one task at a time-has become ubiquitous in everyday life. Understanding of how MT is learned could enable optimizing learning regimes for tasks and occupations that necessitate frequent MT. Previous research has distinguished between MT learning regimes in which all tasks are learned in parallel, single-task (ST) learning regimes in which all tasks are learned individually, and mixed learning regimes (Mix) in which MT and ST regimes are mixed. Research using simple laboratory tasks has consistently shown that MT regimes are the most efficient-the so-called dual-task practice advantage. However, it is currently unclear which learning regimes are used in everyday life, and which regime people would prefer if given a choice. To answer these questions, 72 participants completed an online survey to describe their real-life experiences of MT learning (e.g., when learning to drive), their opinions about learning MT activities, and filled out the Multitasking Preference Inventory to assess polychronicity. Descriptive statistics showed that for everyday activities, particularly learning to drive, Mix regimes were both the most used and most preferred method, whereas MT regimes were the least preferred. A potential explanation is that everyday MT tasks are typically complex, and so people prefer to learn the individual tasks first, before combining the tasks into an MT learning regime. Preference to engage in MT, as assessed by the MPI, positively correlated (Pearson's r = .24) with preference for MT learning regimes, suggesting that individual differences in learning of complex everyday MT activities can be determined. In conclusion, everyday life multitasking activities such as learning to drive are mostly learned in Mix regimes, i.e. a combination of ST and MT training, and people's preference to learn such activities with MT regimes increases with their level of polychronicity.
多任务处理（MT）——即一次执行多项任务——在日常生活中已变得无处不在。了解MT是如何习得的，有助于为那些需要频繁进行MT的任务和职业优化学习模式。先前的研究区分了几种MT学习模式，其中包括所有任务并行学习的模式、所有任务单独学习的单任务（ST）学习模式，以及MT和ST模式混合的混合学习模式（Mix）。使用简单实验室任务的研究一直表明，MT模式是最有效的——即所谓的双任务练习优势。然而，目前尚不清楚日常生活中使用的是哪种学习模式，以及如果可以选择，人们会更喜欢哪种模式。为了回答这些问题，72名参与者完成了一项在线调查，以描述他们在MT学习方面的现实生活经历（例如，学习驾驶时）、他们对学习MT活动的看法，并填写了多任务偏好量表以评估多任务倾向。描述性统计表明，对于日常活动，尤其是学习驾驶，Mix模式既是使用最多的方法，也是最受欢迎的方法，而MT模式是最不受欢迎的。一种可能的解释是，日常MT任务通常很复杂，因此人们更喜欢先单独学习各个任务，然后再将这些任务组合成MT学习模式。通过多任务偏好量表评估的参与MT的偏好与对MT学习模式的偏好呈正相关（皮尔逊相关系数r = 0.24），这表明复杂日常MT活动学习中的个体差异是可以确定的。总之，诸如学习驾驶之类的日常生活多任务活动大多是通过Mix模式习得的，即ST和MT训练的结合，并且人们使用MT模式学习此类活动的偏好会随着他们的多任务倾向水平而增加。
