The neural bases of task proficiency and dual-tasking: Escaping the frontal bottleneck

任务熟练度和双重任务的神经基础：摆脱额叶瓶颈

• 批准号: 1232530
• 负责人: Maximilian Riesenhuber
• 金额: $ 70.59万
• 依托单位: Georgetown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1232530&HistoricalAwards=false
• 关键词: neural; bases; task; proficiency; dual

One of the most impressive aspects of human cognition is its ability to, through practice, convert effortful tasks to those that can be performed with little effort, and even in parallel with other, attention-demanding tasks. The recent proliferation of technologies permitting constant engagement in our culture has made multitasking increasingly prevalent, giving rise to new policy challenges as people's attempts at multitasking exceed their cognitive capabilities, such as when texting while driving. A better understanding of the brain's capabilities and limitations to automate tasks is therefore highly desirable. Yet, the neural bases of automaticity and multitasking are still poorly understood, leading to substantial disagreement about the brain's multitasking capabilities. In particular, while there is broad consensus that learning new tasks is associated with the development of task-selective circuits in the front of the brain which are selectively activated or suppressed depending on the individual's attentional focus, several experiments have shown that this architecture can create a cognitive "bottleneck" in situations when more than one task needs to be executed at the same time. Dr. Maximilian Riesenhuber and his team at Georgetown University are testing the key hypothesis that under certain conditions, familiar tasks can be "offloaded" to circuits in the back of the brain, thus freeing up the frontal system to simultaneously perform additional, attention-demanding tasks. To test this hypothesis, the project will use sophisticated behavioral training paradigms to enable participants to perform more than one task in parallel, and then use a combination of advanced functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) techniques to track how the brain learns to enable multitasking. The study will advance our understanding of the mechanisms underlying the brain's ability to automatize and parallelize task execution, essential in the face of the increasing pressures to multitask in today's culture. The results will also be relevant for a range of societal and medical challenges. For instance, deficits in multitasking and task switching have been associated with schizophrenia and attention deficit hyperactivity disorder. In addition, it is being increasingly appreciated that automaticity itself can be associated with negative consequences, as automatic processes can reduce flexibility in responding due to a lack of conscious awareness and decreased control. For instance, a challenge in treating drug addiction is that many maladaptive behaviors associated with drug abuse become automatized. Even when associated with high performance, automatic task execution can be problematic in domains in which accountability is critical, such as in legal or medical decision-making. Understanding how the brain automatizes tasks is a key stepping stone in making progress in these areas.

人类认知最令人印象深刻的方面之一是，通过练习，它能够将费力的任务转化为不费力的任务，甚至与其他需要注意力的任务并行。最近技术的扩散使我们的文化不断参与，使多任务处理变得越来越普遍，随着人们对多任务处理的尝试超过了他们的认知能力，例如在开车时发短信，这带来了新的政策挑战。因此，更好地了解大脑自动化任务的能力和局限性是非常必要的。然而，自动性和多任务处理的神经基础仍然知之甚少，导致对大脑多任务处理能力的实质性分歧。特别是，虽然有广泛的共识，学习新的任务是与任务选择性电路在大脑前部的发展，这是有选择地激活或抑制取决于个人的注意力集中，几个实验表明，这种架构可以创建一个认知的“瓶颈”的情况下，当一个以上的任务需要在同一时间执行。乔治城大学的马克西米利安博士和他的团队正在测试一个关键假设，即在某些条件下，熟悉的任务可以“卸载”到大脑后部的电路，从而释放额叶系统，同时执行额外的，需要注意力的任务。为了验证这一假设，该项目将使用复杂的行为训练范式，使参与者能够并行执行多项任务，然后使用先进的功能性磁共振成像（fMRI）和脑电图（EEG）技术来跟踪大脑如何学习实现多任务处理。这项研究将促进我们对大脑自动化和并行化任务执行能力的机制的理解，这在当今文化中面临越来越大的多任务压力时至关重要。这些结果也将与一系列社会和医学挑战相关。例如，多任务处理和任务转换的缺陷与精神分裂症和注意缺陷多动障碍有关。此外，越来越多的人认识到，自动性本身可能与负面后果有关，因为自动化过程可能会由于缺乏有意识的意识和控制力下降而降低反应的灵活性。例如，治疗药物成瘾的一个挑战是，许多与药物滥用相关的适应不良行为变得自动化。即使与高性能相关联，自动任务执行也可能在责任感至关重要的领域中存在问题，例如在法律的或医疗决策中。了解大脑如何自动化任务是在这些领域取得进展的关键垫脚石。