Using objective robotic automated performance metrics and task-evoked pupillary response to distinguish surgeon expertise.

PURPOSE: In this study, we investigate the ability of automated performance metrics (APMs) and task-evoked pupillary response (TEPR), as objective measures of surgeon performance, to distinguish varying levels of surgeon expertise during generic robotic surgical tasks. Additionally, we evaluate the association between APMs and TEPR. METHODS: Participants completed ten tasks on a da Vinci Xi Surgical System (Intuitive Surgical, Inc.), each representing a surgical skill type: EndoWrist® manipulation, needle targeting, suturing/knot tying, and excision/dissection. Automated performance metrics (instrument motion tracking, EndoWrist® articulation, and system events data) and TEPR were recorded by a systems data recorder (Intuitive Surgical, Inc.) and Tobii Pro Glasses 2 (Tobii Technologies, Inc.), respectively. The Kruskal-Wallis test determined significant differences between groups of varying expertise. Spearman's rank correlation coefficient measured associations between APMs and TEPR. RESULTS: Twenty-six participants were stratified by robotic surgical experience: novice (no prior experience; n = 9), intermediate (< 100 cases; n = 9), and experts (≥ 100 cases; n = 8). Several APMs differentiated surgeon experience including task duration (p < 0.01), time active of instruments (p < 0.03), linear velocity of instruments (p < 0.04), and angular velocity of dominant instrument (p < 0.04). Task-evoked pupillary response distinguished surgeon expertise for three out of four task types (p < 0.04). Correlation trends between APMs and TEPR revealed that expert surgeons move more slowly with high cognitive workload (ρ < - 0.60, p < 0.05), while novices move faster under the same cognitive experiences (ρ > 0.66, p < 0.05). CONCLUSIONS: Automated performance metrics and TEPR can distinguish surgeon expertise levels during robotic surgical tasks. Furthermore, under high cognitive workload, there can be a divergence in robotic movement profiles between expertise levels.

Brief report: eye-movement patterns during an embedded figures test in children with ASD.

The present study examined fixation frequency and duration during an Embedded Figures Test (EFT) in an effort to better understand the attentional and perceptual processes by which individuals with autism spectrum disorder (ASD) achieve accelerated EFT performance. In particular, we aimed to elucidate differences in the patterns of eye-movement in ASD and typically developing (TD) children, thus providing evidence relevant to the competing theories of weak central coherence (WCC) and enhanced perceptual functioning. Consistent with prior EFT studies, we found accelerated response time (RT) in children with ASD. No group differences were seen for fixation frequency, but the ASD group made significantly shorter fixations compared to the TD group. Eye-movement results indicate that RT advantage in ASD is related to both WCC and enhanced perceptual functioning.

Identifying cognitive state from eye metrics.

INTRODUCTION: This paper describes a new approach for identifying cognitive state by using information obtained only from the eye. Data are collected from cameras mounted on a lightweight headband. A set of eye metrics captures essential eye information from the raw data of pupil size and point-of-gaze. The metrics are easily calculated every second, so that the entire set of metrics can be computed in real time. METHODS: Three studies provide empirical evidence to test whether the eye metrics are sufficient to discriminate between two different cognitive states. The first study examines the states of relaxed and engaged in the context of problem solving. The second study looks at the states of focused and distracted attention in the context of driving. The third study inspects the states of alert and fatigued in the context of visual search. Two statistical models are used to classify cognitive state for all three studies: linear discriminant function analysis and non-linear neural network analysis. Data for the models are eye metrics computed at 1-, 4-, and 10-s intervals. RESULTS: All discriminant function analyses are statistically significant, and classification rates are high. Neural network models have equal or better performance than discriminant function models across all three studies. DISCUSSION: The seven eye metrics successfully discriminate between the states in all studies. Models from individual participants as well as the aggregate model over all participants are successful in identifying cognitive states based on task condition. Classification rates compare favorably with similar studies.

Culture-fair cognitive ability assessment: information processing and psychophysiological approaches.

Valid assessment with diverse populations requires tools that are not influenced by cultural elements. This study investigated the relationships between culture, information processing efficiency, and general cognitive capacities in samples of Caucasian and Mexican American college students. Consistent with the neural efficiency hypothesis, pupillary responses (indexing mental effort) and detection accuracy scores on a visual backward-masking task were both significantly related to the Wechsler Adult Intelligence Scale-Revised (WAIS-R) Full Scale scores. These measures of information processing efficiency were similar in the two groups. However, they were related only to Caucasian American, but not to a comparable sample of Mexican American, students' WAIS-R scores. Therefore, the differential validity in prediction suggests that the WAIS-R test may contain cultural influences that reduce the validity of the WAIS-R as a measure of cognitive ability for Mexican American students. Information processing and psychophysiological approaches may be helpful in developing culture-fair cognitive ability measures.

Pupillary responses on the visual backward masking task reflect general cognitive ability.

Cognitive processing efficiency requires both an ability to attend to task-relevant stimuli with quickness and accuracy, also while filtering distracting or task-irrelevant stimuli. This study investigated cognitive processing efficiency by using pupillary responses as an index of attentional allocation to relevant target and irrelevant masks on a visual backward masking task. The relationship between attentional allocation on this task and general cognitive ability on the scholastic aptitude test (SAT) was examined in college students (n=67). A principle components analysis of the pupillary response waveform isolated a late component that appeared to index the attentional demands associated with processing masks on the backward masking task. This pupillary response index of wasteful resource allocation to the mask accounted for significant variance in SAT scores over and above that accounted for by socio-economic status and target detection accuracy scores. Consistent with the neural efficiency hypothesis, individuals who allocated more resources to processing irrelevant information performed more poorly on cognitive ability tests.