Abstract
Functional near-infrared spectroscopy (fNIRS) provides reliable results for determining cognitive load based on averaged cortical blood flow during multiple repetitions of short cognitive tasks. At the same time, it remains unclear how to use this technique for assessing cognitive load during prolonged single-trial activity. In this study, we used a computer-based emergency simulation game for inducing different levels of cognitive load. We propose a novel approach to measure cognitive load using specific time slots, determined based on simulation log-data interpreted in light of Barrouillet's time-based resource-sharing model. To validate this approach, we compared cortical activity in dorsolateral prefrontal cortex (DLPFC) and left inferior frontal gyrus (IFG) regions measured at four specific time slots during a simulation. We found significant associations between cognitive load and neuronal activity within the DLPFC depending on the chosen time slot, whereas no such dependencies were found for the IFG. These results illustrate how knowledge of task structure could be used advantageously for the identification of cognitive load. Although requiring further investigation in terms of reliability and generalizability, the presented approach can be considered promising evidence that fNIRS might be suitable for more general reliable assessments of cognitive load during prolonged single-trial activities and for real-time adaptations in simulation-based learning environments.
Highlights
D AILY life is becoming increasingly automatedAutopilots, speed and lane assistants, etc. help us to deal with well-controlled and predictable settings
We found a significantly positive effect of real difficulty on hemodynamic cortical activation in DLPFC-L and DLPFC-R, meaning that higher levels of real difficulty were associated with higher hemodynamic activity
Performance was significantly negatively associated with hemodynamic cortical activation in the DLPFC-R, meaning that higher level of performance was associated with lower hemodynamic activity in this ROI (Appendix: Table III)
Summary
D AILY life is becoming increasingly automatedAutopilots, speed and lane assistants, etc. help us to deal with well-controlled and predictable settings. As it is not trivial to set up an analog training environment for life- and time-critical emergencies, computeraided simulations, and digital training scenarios can be used advantageously in this area [1,2,3]. Along with the potential to simulate dangerous time-critical situations, digital training scenarios allow for the collection of individual data, which can be used to model cognitive or emotional states of the user [4] including cognitive load. Human-machine interaction such as any computer-based training, might be optimized using a monitoring system capable of detecting variations in cognitive load [11], which seems feasible in real-world training environments [12, 13]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
