This paper discusses how the fields of augmented cognition and neuroergonomics can be expanded into training. Several classification algorithms based upon EEG data and occular data are discussed in terms of their ability to classify operator state in real time. These indices have been shown to enhance operator performance within adaptive automation paradigms. Learning is different from performing a task that one is familiar with. According to cognitive load theory (CLT), learning is essentially the act of organizing information from working memory into long term memory. However, our working memory system has a bottleneck in this process, such that when training exceeds working memory capacity, learning is hindered. This paper discusses how CLT can be combined with multiple resource theory to create a model of adaptive training. This new paradigm hypothesizes that a system that can monitor working memory capacity in real time and adjust training difficulty can improve learning.
The use of eye metrics to predict the state of one's mental workload involves reliable and accurate modeling techniques. This study assessed the workload classification accuracy of three data mining techniques; artificial neural network (ANN), logistic regression, and classification tree. The results showed that the selection of model technique and the interaction between model type and time segmentation have significant effects on the ability to predict an individual's mental workload during a recall task. The ANN and classification tree both performed much better than logistic regression with 1-s incremented data. The classification tree also performed much better with data averaged over the full recall task. In addition, the transparency of the classification tree showed that pupil diameter and divergence are significantly more important predictors than fixation when modeling 1-s incremented data.
Electroencephalography (EEG) has the prospect of providing a means to gauge operator workload in a manner that does not intrude on the task being performed. Specifically, it has been proposed that the technique could be used as a method to speed the learning of a task, by adjusting the task to suit the state of the learner. The present study recorded EEG while participants performed a simulated Unmanned Aerial Vehicle (UAV) reconnaissance task. Analysis of power in three EEG frequency bands of interest found differences between the types of task being performed; however more complex analysis may be necessary to discern levels of difficulty within the task.
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