Abstract
Recent advances in neuroimaging technologies have rendered multimodal analysis of operators’ cognitive processes in complex task settings and environments increasingly more practical. In this exploratory study, we utilized optical brain imaging and mobile eye tracking technologies to investigate the behavioral and neurophysiological differences among expert and novice operators while they operated a human-machine interface in normal and adverse conditions. In congruence with related work, we observed that experts tended to have lower prefrontal oxygenation and exhibit gaze patterns that are better aligned with the optimal task sequence with shorter fixation durations as compared to novices. These trends reached statistical significance only in the adverse condition where the operators were prompted with an unexpected error message. Comparisons between hemodynamic and gaze measures before and after the error message indicated that experts’ neurophysiological response to the error involved a systematic increase in bilateral dorsolateral prefrontal cortex (dlPFC) activity accompanied with an increase in fixation durations, which suggests a shift in their attentional state, possibly from routine process execution to problem detection and resolution. The novices’ response was not as strong as that of experts, including a slight increase only in the left dlPFC with a decreasing trend in fixation durations, which is indicative of visual search behavior for possible cues to make sense of the unanticipated situation. A linear discriminant analysis model capitalizing on the covariance structure among hemodynamic and eye movement measures could distinguish experts from novices with 91% accuracy. Despite the small sample size, the performance of the linear discriminant analysis combining eye fixation and dorsolateral oxygenation measures before and after an unexpected event suggests that multimodal approaches may be fruitful for distinguishing novice and expert performance in similar neuroergonomic applications in the field.
Highlights
Understanding the neural underpinnings of complex cognitive tasks in the context of safety-critical settings is a key objective for neuroergonomics research (Parasuraman, 2011; Parasuraman et al, 2012; Mehta and Parasuraman, 2013)
Cognitive or mental workload is an essential determinant of operator performance that needs to be optimized through effective training and user-centered design practices
In this study, we explored the simultaneous use of functional near-infrared spectroscopy (fNIRS) and eye tracking in a human-machine interaction scenario involving a military land platform
Summary
Understanding the neural underpinnings of complex cognitive tasks in the context of safety-critical settings is a key objective for neuroergonomics research (Parasuraman, 2011; Parasuraman et al, 2012; Mehta and Parasuraman, 2013). Cognitive workload addresses the limited capacity of the human brain for processing information demanded by the task at hand (Wickens and McCarley, 2007). Decrements in performance, as evidenced in missed responses or delayed response times, are typically observed when operators are subjected to cognitive workload beyond the maximum information processing capacity of their brains (Hancock and Parasuraman, 1992; Wickens et al, 2013). Cognitive workload is an essential determinant of cognitive performance These limits are subject to change due to the development of expertise during training, and design elements that promote or hinder the utilization of the system’s affordances, which altogether make their assessment a challenging issue (Fairclough et al, 2005; Ullén et al, 2018)
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