Perception in Action Research Centre, Faculty of Human Sciences, Macquarie University, Sydney, Australia.
Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.
Elife. 2021 Apr 8;10:e60563. doi: 10.7554/eLife.60563.
There are many monitoring environments, such as railway control, in which lapses of attention can have tragic consequences. Problematically, sustained monitoring for rare targets is difficult, with more misses and longer reaction times over time. What changes in the brain underpin these 'vigilance decrements'? We designed a multiple-object monitoring (MOM) paradigm to examine how the neural representation of information varied with target frequency and time performing the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for a frequent target (active) condition. This was mirrored in neural decoding using magnetoencephalography: coding of critical information declined more during monitoring versus active conditions along the experiment. We developed new analyses that can predict behavioural errors from the neural data more than a second before they occurred. This facilitates pre-empting behavioural errors due to lapses in attention and provides new insight into the neural correlates of vigilance decrements.
有许多监控环境,如铁路控制,其中注意力的疏忽可能会产生悲惨的后果。有问题的是,对稀有目标的持续监控是很困难的,随着时间的推移,错过的目标会更多,反应时间也会更长。这些“警惕性下降”的大脑会发生什么变化?我们设计了一个多目标监测(MOM)范式,以研究信息的神经表现如何随目标频率和执行任务的时间而变化。对于稀有目标(监测)条件,行为表现随着时间的推移而下降,但对于频繁目标(主动)条件则不会。这在使用脑磁图进行神经解码中得到了反映:与主动条件相比,在监测条件下,关键信息的编码在实验过程中下降得更多。我们开发了新的分析方法,可以在行为错误发生前超过一秒的时间从神经数据中预测行为错误。这有助于预先防范由于注意力不集中而导致的行为错误,并为警惕性下降的神经相关性提供了新的见解。