Cengiz Acarturk

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.

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.

The popular debates about the future organization of work through artificial intelligence technologies focus on the replacement of human beings by novel technologies. In this essay, we oppose this statement by closely following what has been developed as AI technologies and analyzing how they work, specifically focusing on research that may impact work organizations. We develop this argument by showing that the recent research and developments in AI technologies focus on developing accurate and precise performance models, which in turn shapes organizational patterns of work. We propose that the increased interest in the relationship between human cognition and performance will shortly bring human cognition to the focus on AI systems in workplaces. More specifically, we claim that the cognitive load measurement will shape human performance in manufacturing systems shortly.

Cognitive science was established as an interdisciplinary domain of research in the 1970s. Since then, the domain has flourished, despite disputes concerning its interdisciplinarity. Multiple methods exist for the assessment of interdisciplinary research. The present study proposes a methodology for quantifying interdisciplinary aspects of research in cognitive science. We propose models for text similarity analysis that provide helpful information about the relationship between publications and their specific research fields, showing potential as a robust measure of interdisciplinarity. We designed and developed models utilizing the Doc2Vec method for analyzing cognitive science and related fields. Our findings reveal that cognitive science collaborates closely with most constituent disciplines. For instance, we found a balanced engagement between several constituent fields—including psychology, philosophy, linguistics, and computer science—that contribute significantly to cognitive science. On the other hand, anthropology and neuroscience have made limited contributions. In our analysis, we find that the scholarly domain of cognitive science has been exhibiting overt interdisciplinary for the past several decades.