Abstract
The ability to distinguish between high and low levels of task engagement in the real world is important for detecting and preventing performance decrements during safety-critical operational tasks. We therefore investigated whether functional Near Infrared Spectroscopy (fNIRS), a portable brain neuroimaging technique, can be used to distinguish between high and low levels of task engagement during the performance of a selective attention task. A group of participants performed the multi-source interference task (MSIT) while we recorded brain activity with fNIRS from two brain regions. One was a key region of the “task-positive” network, which is associated with relatively high levels of task engagement. The second was a key region of the “task-negative” network, which is associated with relatively low levels of task engagement (e.g., resting and not performing a task). Using activity in these regions as inputs to a multivariate pattern classifier, we were able to predict above chance levels whether participants were engaged in performing the MSIT or resting. We were also able to replicate prior findings from functional magnetic resonance imaging (fMRI) indicating that activity in task-positive and task-negative regions is negatively correlated during task performance. Finally, data from a companion fMRI study verified our assumptions about the sources of brain activity in the fNIRS experiment and established an upper bound on classification accuracy in our task. Together, our findings suggest that fNIRS could prove quite useful for monitoring cognitive state in real-world settings.
Highlights
The ability to distinguish between high and low levels of task engagement is important for detecting and preventing performance decrements during safety-critical operational tasks in the real world
Data from a companion functional magnetic resonance imaging (fMRI) study verified our assumptions about the sources of brain activity in the functional Near Infrared Spectroscopy (fNIRS) experiment and established an upper bound on classification accuracy in our task
We found that multivariate pattern classification techniques could distinguish between periods of task performance and periods of rest based on brain activity recorded from (a) different regions of the dorsolateral prefrontal cortex (DLPFC) in the task-positive network or (b) the DLPFC in the task-positive network and the MFG in the task-negative network
Summary
The ability to distinguish between high and low levels of task engagement is important for detecting and preventing performance decrements during safety-critical operational tasks in the real world. Examples of such tasks include commercial aviation, monitoring for air traffic control, executing space walks, performing surgery, and driving. Since accident-causing errors can be made even by skilled professionals (Dismukes et al, 2007), the ability to monitor cognitive state measures for low levels of task engagement in real time could be useful for developing an “early warning system” for detecting and preventing performance errors before they occur. The ability to monitor cognitive state for low levels of task engagement could be helpful for detecting and preventing vigilance decrements due to sleep-deprivation (Drummond et al, 2005; De Havas et al, 2012) or distraction (Strayer et al, 2011)
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