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My Brain Is Out of the Loop: A Neuroergonomic Approach of OOTL Phenomenon

机译:我的大脑不在循环中:OOTL现象的神经工程学方法

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The world surrounding us has become increasingly technological. Nowadays, the influence of automation is perceived in each aspect of everyday life and not only in the world of industry. Automation certainly makes some aspects of life easier, faster and safer. Nonetheless, empirical data suggests that traditional automation has many negative performance and safety consequences. Particularly, in cases of automatic equipment failure, human supervisors seemed effectively helpless to diagnose the situation, determine the appropriate solution and retake control, a set of difficulties called the "out-of-the-loop" (OOL) performance problem. Because automation is not powerful enough to handle all abnormalities, this difficulty in "takeover" is a central problem in automation design. The OOL performance problem represents a key challenge for both systems designers and human factor society. After decades of research, this phenomenon remains difficult to grasp and treat and recent tragic accidents remind us the difficulty for human operator to interact with highly automated system. The general objective of our research project is to improve our comprehension of the OOL performance problem. To address this issue, we aim (1) to identify the neuro-functional correlates of the OOL performance problem, (2) to propose design recommendations to optimize human-automation interaction and decrease OOL performance problem occurrence. Behavioral data and brain imaging studies will be used to provide a better understanding of this phenomenon at both physiological and psychological levels.
机译:我们周围的世界变得越来越技术化。如今,自动化的影响不仅在工业领域中,而且在日常生活的各个方面都得到了体现。自动化无疑使生活的某些方面更加轻松,快捷和安全。但是,经验数据表明,传统自动化具有许多负面性能和安全后果。特别是在自动设备出现故障的情况下,人类主管似乎实际上无助于诊断情况,确定适当的解决方案和重新控制,这是称为“环外”(OOL)性能问题的一系列困难。由于自动化的功能不足以处理所有异常情况,因此“接管”中的这一困难是自动化设计中的中心问题。 OOL性能问题对系统设计人员和人为因素社会都构成了关键挑战。经过数十年的研究,这种现象仍然难以掌握和治疗,最近发生的悲剧性事故提醒我们操作人员难以与高度自动化的系统进行交互。我们研究项目的总体目标是提高我们对OOL性能问题的理解。为了解决这个问题,我们的目标是(1)识别OOL性能问题的神经功能相关性;(2)提出设计建议,以优化人与人之间的互动并减少OOL性能问题的发生。行为数据和脑成像研究将用于在生理和心理层面上更好地理解这一现象。

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