On the Cost of Detection Response Task Performance on Cognitive Load

Biondi, Francesco; Balachandran, Balakumar; Ayare, Prathamesh

doi:10.1177/0018720820931628

Cited by 18 publications

(12 citation statements)

References 17 publications

(24 reference statements)

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“…To our surprise in this project, we found that a significant difference was only displayed in the maxAPS between easy and difficult tasks, not in the minAPS, meanAPS, medianAPS, and max interval index [ 22 ]. These are not duplicating results from previous studies [ 15 , 23 , 24 ]. A possible explanation is that most of the easy subtasks including identification of landmarks, disinfection, local anesthesia, and incision, are at the beginning of the procedure.…”

Section: Discussionsupporting

confidence: 65%

When medical trainees encountering a performance difficulty: evidence from pupillary responses

et al. 2022

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Background Medical trainees are required to learn many procedures following instructions to improve their skills. This study aims to investigate the pupillary response of trainees when they encounter moment of performance difficulty (MPD) during skill learning. Detecting the moment of performance difficulty is essential for educators to assist trainees when they need it. Methods Eye motions were recorded while trainees practiced the thoracostomy procedure in the simulation model. To make pupillary data comparable among trainees, we proposed the adjusted pupil size (APS) normalizing pupil dilation for each trainee in their entire procedure. APS variables including APS, maxAPS, minAPS, meanAPS, medianAPS, and max interval indices were compared between easy and difficult subtasks; the APSs were compared among the three different performance situations, the moment of normal performance (MNP), MPD, and moment of seeking help (MSH). Results The mixed ANOVA revealed that the adjusted pupil size variables, such as the maxAPS, the minAPS, the meanAPS, and the medianAPS, had significant differences between performance situations. Compared to MPD and MNP, pupil size was reduced during MSH. Trainees displayed a smaller accumulative frequency of APS during difficult subtask when compared to easy subtasks. Conclusions Results from this project suggest that pupil responses can be a good behavioral indicator. This study is a part of our research aiming to create an artificial intelligent system for medical trainees with automatic detection of their performance difficulty and delivering instructional messages using augmented reality technology.

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Section: Discussionsupporting

confidence: 65%

When medical trainees encountering a performance difficulty: evidence from pupillary responses

et al. 2022

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“…We also use SNR to investigate the sensitivity of pupil size and DRT response times in tracking increasing levels of cognitive task demand (objective 2). To tackle these objectives, we used the dataset collected in Biondi et al (2021).…”

Section: Discussionmentioning

confidence: 99%

“…Each of the seven experimental conditions lasted approximately 5 minutes. The data used in this study is a subset of the larger dataset collected in Biondi et al (2021).…”

Section: Procedures and Designmentioning

confidence: 99%

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Using Signal-to-Noise Ratio to Explore The Cognitive Cost of The Detection Response Task

Pillai

Balachandran

Biondi

2022

Proceedings of the Human Factors and Ergonomics Society Annual

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The Detection Response Task (DRT) is a standardized measure of cognitive load requiring manual responses to intermittent stimuli. Given its simplicity, it is hypothesized that its completion will not interfere with the primary task. However, recent studies challenge this assumption showing a definite cost of DRT performance. In this study we adopt signal-to-noise ratio (SNR), a measure commonly used in communication engineering: 1) to explore the cognitive cost of DRT 2) to compare the sensitivity of DRT performance and pupil size in measuring cognitive load. SNR was calculated using the data from a study wherein DRT performance and pupil size were recorded while participants completed increasingly difficult mental tasks. We conclude that DRT completion interfered with the overall cognitive task demand and showed pupil size’s greater sensitivity to changes in cognitive load. Though exploratory, our study advances using SNR as a powerful tool for data integration in HF/E research.

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“…Past researches on eye-tracking have proved that eye signals can report performers' visual searching strategies (He et al, 2020), eye-hand coordination patterns, and workload levels (Biondi, Balasingam, & Ayare, 2020;Henneman, Marquard, Fisher, & Gawlinski, 2017). Our goal is to develop an AI-based teaching platform for training healthcare skills with minimal engagement from faculty members.…”

Section: Discussionmentioning

confidence: 99%

Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking

Liu

Zheng

Duan

et al. 2021

JEMR

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Eye-tracking can help decode the intricate control mechanism in human performance. In healthcare, physicians-in-training requires extensive practice to improve their healthcare skills. When a trainee encounters any difficulty in the practice, they will need feedback from experts to improve their performance. The personal feedback is time-consuming and subjected to bias. In this study, we tracked the eye movements of trainees during their colonoscopic performance in simulation. We applied deep learning algorithms to detect the eye-tracking metrics on the moments of navigation lost (MNL), a signature sign for performance difficulty during colonoscopy. Basic human eye gaze and pupil characteristics were learned and verified by the deep convolutional generative adversarial networks (DCGANs); the generated data were fed to the Long Short-Term Memory (LSTM) networks with three different data feeding strategies to classify MNLs from the entire colonoscopic procedure. Outputs from deep learning were compared to the expert’s judgment on the MNLs based on colonoscopic videos. The best classification outcome was achieved when we fed human eye data with 1000 synthesized eye data, where accuracy (90%), sensitivity (90%), and specificity (88%) were optimized. This study built an important foundation for our work of developing a self-adaptive education system for training healthcare skills using simulation.

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On the Cost of Detection Response Task Performance on Cognitive Load

Cited by 18 publications

References 17 publications

When medical trainees encountering a performance difficulty: evidence from pupillary responses

When medical trainees encountering a performance difficulty: evidence from pupillary responses

Using Signal-to-Noise Ratio to Explore The Cognitive Cost of The Detection Response Task

Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking

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