Quantification of baseline pupillary response and task-evoked pupillary response during constant and incremental task load

Baseline pupil size encodes task-related information and modulates the task-evoked response in a speech-in-noise task

Pupillometry data are commonly reported relative to a baseline value recorded in a controlled pre-task condition. In this study, the influence of the experimental design and the preparatory processing related to task difficulty on the baseline pupil size was investigated during a speech intelligibility in noise paradigm. Furthermore, the relationship between the baseline pupil size and the temporal dynamics of the pupil response was assessed. The analysis revealed strong effects of block presentation order, within-block sentence order and task difficulty on the baseline values. An interaction between signal-to-noise ratio and block order was found, indicating that baseline values reflect listener expectations arising from the order in which the different blocks were presented. Furthermore, the baseline pupil size was found to affect the slope, delay and curvature of the pupillary response as well as the peak pupil dilation. This suggests that baseline correction might be sufficient when reporting pupillometry results in terms of mean pupil dilation only, but not when a more complex characterization of the temporal dynamics of the response is considered. By clarifying which factors affect baseline pupil size and how baseline values interact with the task-evoked response, the results from the present study can contribute to a better interpretation of the pupillary response as a marker of cognitive processing.

Assessment of Radiation Therapy Technologists' Workload and Situation Awareness: Monitoring 2 Versus 3 Collocated Display Monitors

Purpose

This study aimed to assess the effect of monitoring 2 versus 3 collocated displays on radiation therapist technologists’ (RTTs) workload (WL) and situation awareness (SA) during routine treatment delivery tasks.

Methods and Materials

Seven RTTs completed 4 simulated treatment delivery scenarios (2 scenarios per experimental condition; 2 vs 3 collocated displays) in a within-subject experiment. WL was subjectively measured using the National Aeronautics and Space Administration (NASA) Task Load Index, and objectively measured using eye activity measures. SA was subjectively measured using the SA rating technique, and objectively measured using the SA global assessment technique. Two-tailed paired t tests were conducted to test for differences in means when parametric assumptions were satisfied, otherwise Wilcoxon signed-rank tests were conducted. A .05 level of significance was applied to all statistical tests.

Results

No statistically and clinically significant differences were observed between monitoring 2 versus 3 monitors on eye tracking measures (blink rate: 9.4 [4.8] vs 9.6 [4.0]; task evoked pupillary response: 0.16 [0.14] vs 0.21 [0.15]; NASA Task Load Index: 34.7 [19.8] vs 35.3 [20.4]; SA rating technique: 19.3 [6.2] vs 19.5 [7.0]; and SA global assessment technique scores: 100 [0] vs 100 [0]).

Conclusions

Our preliminary findings suggest that monitoring 3 collocated displays by 1 RTT does not impact WL and SA compared with monitoring 2 collocated displays. Only 2 of many possible configurations were investigated. If institutions removed the 3rd display based on the results of this study, there could be unforeseen error(s) if that display helped in situations not assessed in this study.

Sensor-based indicators of performance changes between sessions during robotic surgery training

Training of surgeons is essential for safe and effective use of robotic surgery, yet current assessment tools for learning progression are limited. The objective of this study was to measure changes in trainees' cognitive and behavioral states as they progressed in a robotic surgeon training curriculum at a medical institution. Seven surgical trainees in urology who had no formal robotic training experience participated in the simulation curriculum. They performed 12 robotic skills exercises with varying levels of difficulty repetitively in separate sessions. EEG (electroencephalogram) activity and eye movements were measured throughout to calculate three metrics: engagement index (indicator of task engagement), pupil diameter (indicator of mental workload) and gaze entropy (indicator of randomness in gaze pattern). Performance scores (completion of task goals) and mental workload ratings (NASA-Task Load Index) were collected after each exercise. Changes in performance scores between training sessions were calculated. Analysis of variance, repeated measures correlation, and machine learning classification were used to diagnose how cognitive and behavioral states associate with performance increases or decreases between sessions. The changes in performance were correlated with changes in engagement index (rrm=-.25,p<.001) and gaze entropy (rrm=-.37,p<.001). Changes in cognitive and behavioral states were able to predict training outcomes with 72.5% accuracy. Findings suggest that cognitive and behavioral metrics correlate with changes in performance between sessions. These measures can complement current feedback tools used by medical educators and learners for skills assessment in robotic surgery training.

Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training

OBJECTIVE

The aim of this study is to assess the relationship between eye-tracking measures and perceived workload in robotic surgical tasks.

BACKGROUND

Robotic techniques provide improved dexterity, stereoscopic vision, and ergonomic control system over laparoscopic surgery, but the complexity of the interfaces and operations may pose new challenges to surgeons and compromise patient safety. Limited studies have objectively quantified workload and its impact on performance in robotic surgery. Although not yet implemented in robotic surgery, minimally intrusive and continuous eye-tracking metrics have been shown to be sensitive to changes in workload in other domains.

METHODS

Eight surgical trainees participated in 15 robotic skills simulation sessions. In each session, participants performed up to 12 simulated exercises. Correlation and mixed-effects analyses were conducted to explore the relationships between eye-tracking metrics and perceived workload. Machine learning classifiers were used to determine the sensitivity of differentiating between low and high workload with eye-tracking features.

RESULTS

Gaze entropy increased as perceived workload increased, with a correlation of .51. Pupil diameter and gaze entropy distinguished differences in workload between task difficulty levels, and both metrics increased as task level difficulty increased. The classification model using eye-tracking features achieved an accuracy of 84.7% in predicting workload levels.

CONCLUSION

Eye-tracking measures can detect perceived workload during robotic tasks. They can potentially be used to identify task contributors to high workload and provide measures for robotic surgery training.

APPLICATION

Workload assessment can be used for real-time monitoring of workload in robotic surgical training and provide assessments for performance and learning.