Abstract
Ocular activity is known to be sensitive to variations in mental workload, and recent studies have successfully related the distribution of eye fixations to the mental load. This study aimed to verify the effectiveness of the spatial distribution of fixations as a measure of mental workload and its sensitivity to different types of demands imposed by the task: mental, temporal, and physical. To test the research hypothesis, two experimental studies were run: Experiment 1 evaluated the sensitivity of an index of spatial distribution (Nearest Neighbor Index; NNI) to changes in workload. A sample of 30 participants participated in a within-subject design with different types of task demands (mental, temporal, physical) applied to Tetris game; Experiment 2 investigated the accuracy of the index through the analysis of 1-min epochs during the execution of a visual-spatial task (the “spot the differences” puzzle game). Additionally, NNI was compared to a better-known ocular mental workload index, the entropy rate. The data analysis showed a relation between the NNI and the different workload levels imposed by the tasks. In particular: Experiment 1 demonstrated that increased difficulty, due to higher temporal demand, led to a more dispersed pattern with respect to the baseline, whereas the mental demand led to a more grouped pattern of fixations with respect to the baseline; Experiment 2 indicated that the entropy rate and the NNI show a similar pattern over time, indicating high mental workload after the first minute of activity. That suggests that NNI highlights the greater presence of fixation groups and, accordingly, the entropy indicates a more regular and orderly scanpath. Both indices are sensitive to changes in workload and they seem to anticipate the drop in performance. However, the entropy rate is limited by the use of the areas of interest, making it impossible to apply it in dynamic contexts. Conversely, NNI works with the entire scanpath and it shows sensitivity to different types of task demands. These results confirm the NNI as a measure applicable to different contexts and its potential use as a trigger in adaptive systems implemented in high-risk settings, such as control rooms and transportation systems.
Highlights
In high-risk environments characterized by highly dynamic, unpredictable, and uncertain events, many visual elements displayed in the complex control interfaces tax operator’s attention causing cognitive overload
The results showed an increase in the NASA-TLX values of the single subscales matching the respective manipulation
More important to our aims, the analysis of the fixations pattern showed high clustering when the taskload increment was obtained by changing the mental demand, and low clustering when it was obtained by changing the temporal demand
Summary
In high-risk environments characterized by highly dynamic, unpredictable, and uncertain events, many visual elements displayed in the complex control interfaces (e.g., monitoring sensors and warning indicators) tax operator’s attention causing cognitive overload. The restricted attentional capacity of the human being constitutes a well-known ‘‘bottleneck’’ that has been the object of many studies on human information processing (Marois and Ivanoff, 2005; Wolfe et al, 2006; Tombu et al, 2011) and on operator functional state, that is ‘‘the intrinsic relationship between human task performance and the background state of the individual’’ (Hockey et al, 2003). The main objective of studies on human performance and information processing is to reduce the possibility of cognitive overload as much as possible. To avoid this, finding real-time indexes of the operator functional state has become crucial. This should be accomplished by using effective and non-intrusive tools to be applied in high-risk environments
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