Pupillometry is commonly used in research to determine how much mental effort an individual is exerting while completing tasks. Traditionally, larger pupils are associated with increased mental effort when completing more difficult tasks. However, little research has investigated how pupils change as individuals learn a new task. In theory, as one repeatedly completes a task, the task demands should reduce, reliance on working memory should decrease, and the task should become more automatic. This should translate to faster completion times and smaller peak pupil dilations. We tested this hypothesis by having participants complete multiple trials of a cognitive task that requires individuals to orient themselves in space relative to a target. We found that trial completion times and maximum pupil size significantly reduced across trials. These data suggest that measuring changes in pupil dilation may help researchers determine whether individuals have shifted from a learned procedure to an automatic processing of information when learning a new task.

Physiological assessment of cognitive processes has become a topic of increased interest. The value of understanding and measuring brain function at work has the potential to improve performance. The emphasis of this paper is to discuss how pupil diameter can be applied to learning. The link between pupil diameter and task difficulty, or cognitive load, has been repeatedly demonstrated for the past 40 years. However there has been little work to date on measuring cognitive load during training or looking at how real time metrics of cognitive load could be used to adapt training. According to Cognitive Load Theory, cognitive load should be reduced as an individual learns a task and he/she relies more on long term memory than working memory. Ten participants completed a simulated unmanned aerial vehicle task in which they had to identify targets and report their direction of movement. There were three levels of increased difficulty. As expected, pupil diameter significantly dropped within each block as participants learned the task, and then increased again at the start of the next level of difficulty. The results suggest that pupil diameter may be a useful metric for assessing when an individual has transferred information into long term memory. Implications for how pupil diameter can be used to drive an adaptive training system are discussed.

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.

Eye tracking technologies are being utilized at increasing rates within industry and research due to the very recent availability of low cost systems. This paper presents results from a study assessing two eye tracking systems, Gazepoint GP3 and Eye Tribe, both of which are available for under $500 and provide streaming gaze and pupil size data. The emphasis of this research was in evaluating the ability of these eye trackers to identify changes in pupil size which occur as a function of variations in lighting conditions as well as those associated with workload. Ten volunteers participated in an experiment in which a digit span task was employed to manipulate workload as user’s fixated on a monitor which varied in background luminance (black, gray and white). Results revealed that both systems were able to significantly differentiate pupil size differences in high and low workload trials and changes due to the monitor’s luminance. These findings are exceedingly promising for human factors researchers, as they open up the opportunity to augment studies with non-obtrusive, streaming measures of mental workload with technologies available for as little as $100.

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