Cognitive burden estimation for visuomotor learning with fNIRS.
Cognitive burden estimation for visuomotor learning with fNIRS.
复制标题
使用 fNIRS 进行视觉运动学习的认知负担估计。
DOI：
10.1007/978-3-642-15711-0_40
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
James DR
中科院分区：
文献类型：
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作者：
James DR
在外科手术中使用的新型机器人技术需要评估它们对用户以及技术性能的影响。本文采用功能近红外光谱（fNIRS）和图论相结合的方法对视觉运动学习过程中“认知负担”的演变进行了量化。结果表明，在学习的中间阶段，被激活的皮层网络的成本不断上升，表现为认知负担的增加。图论和近红外光谱的这一创新应用使得对支撑任务执行的大脑行为以及新技术和学习如何影响大脑行为的经济评估成为可能。因此，这可能会揭示机器人技术如何改善人机交互和增强微创手术技能的获取。这项工作对于在皮层水平上开发和评估新兴机器人技术以及从区域间皮层连接的角度阐明学习相关的可塑性具有重要意义。
Novel robotic technologies utilised in surgery need assessment for their effects on the user as well as on technical performance. In this paper, the evolution in‘cognitive burden’across visuomotor learning is quantified using a combination of functional near infrared spectroscopy (fNIRS) and graph theory. The results demonstrate escalating costs within the activated cortical network during the intermediate phase of learning which is manifest as an increase in cognitive burden. This innovative application of graph theory and fNIRS enables the economic evaluation of brain behaviour underpinning task execution and how this may be impacted by novel technology and learning. Consequently, this may shed light on how robotic technologies improve human-machine interaction and augment minimally invasive surgical skills acquisition. This work has significant implications for the development and assessment of emergent robotic technologies at cortical level and in elucidating learning-related plasticity in terms of inter-regional cortical connectivity.