Browsing by Subject "eye movements"

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    Auditory Attentional Disengagement in Children with Autism Spectrum Disorder
    (Springer, 2019-06-14) Keehn, Brandon; Kadlaskar, Girija; McNally Keehn, Rebecca; Francis, Alexander L.; Pediatrics, School of Medicine
    Despite early differences in orienting to sounds, no study to date has investigated whether children with ASD demonstrate impairments in attentional disengagement in the auditory modality. Twenty-one nine- to fifteen year old children with ASD and 20 age- and IQ-matched TD children were presented with an auditory gap-overlap paradigm. Evidence of impaired disengagement in ASD was mixed. Differences in saccadic reaction time for overlap and gap conditions did not differ between groups. However, children with ASD did show increased no-shift trials in the overlap condition, as well as reduced disengagement efficiency compared to their TD peers. These results provide further support for disengagement impairments in ASD, and suggest that these deficits include disengaging from and shifting to unimodal auditory information.
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    Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training
    (Sage, 2019) Wu, Chuhao; Cha, Jackie; Sulek, Jay; Zhou, Tian; Sundaram, Chandru P.; Wachs, Juan; Yu, Denny; Urology, School of Medicine
    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.