Sensitivity of the Spatial Distribution of Fixations to Variations in the Type of Task Demand and Its Relation to Visual Entropy

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.

An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing—Demonstration of a Semi-autonomous Suction Tool

“…The physiological differences between the two task conditions suggest that the designed high MWL hemorrhaging condition evoked high MWL. In the hemorrhage case, longer scan path (i.e., dispersed fixations) and higher NNI (i.e., random fixations) during visual exploration have been associated with higher MWL (Maggi & di Nocera, 2021). The significant changes in these metrics may be caused by differences in surgical field visibility under the two conditions.…”

A Neurotechnological Aid For Semi-Autonomous Suction In Robotic-Assisted Surgery

“…First, the raw signals were divided into 15 seconds epochs to extract 5 features related to workload: (1) average pupil diameter PD, (2) number of fixations (NF), (3) average fixation time( FT ), (4) scan path length (SSP), and (5) nearest neighbor index (NNI). The following metrics were selected since they have been previously related to mental workload demands 27 . Fifteen seconds was the minimum epoch size to calculate the eye tracker features based on fixations.…”

“…Last, the nearest neighbor index was calculated as the ratio of the nearest neighbor distance of fixations d(NN) and the average distance of a randomly distributed set of fixations d(ran). The nearest neighbor distance, d(NN), was calculated by applying equation 3 to the fixations set 27 . d(ran) was calculated by applying 3 to a randomly generated set of fixations.…”