Tomomi Okano
ABSTRACT
The relationship between ocular metrics and factor ratings for mental workloads are examined using a Bayesian statistical state-space modeling technique. During a visual search task experiment, microsaccade frequency and pupil size were observed as measures of mental workload. Individual mental workloads were measured using a 6 factor NASA-TLX rating scale. The models calculated generalized temporal changes of microsaccade frequency and pupil size during tasks. The contributions of factors of mental workload are examined using effect size. Also, chronological analysis was introduced to detect the reactions of the metrics during tasks. The results suggest that the response selectivity of microsaccades and pupil size can be used as factors of mental workload.
- Samantha F. Anderson, Ken Kelly, and Scott E. Maxwell. 2017. Sample-Size Planning for More Accurate Statistical Power: A Method Adjusting Sample Effect Sizes for Publication Bias and Uncertainty. Psychological Science 28(11) (2017), 1547–1562.Google Scholar
- Andrew T. Duchowski, Krzysztof Krejtz, Izabela Krejtz, Cezary Biele, Anna Niedzielska, Peter Kiefer, Martin Raubal, and Ioannis Giannopoulos. 2018. The Index of Pupillary Activity: Measuring Cognitive Load vis-à-vis Task Difficulty with Pupil Oscillation. CHI2018.Google ScholarDigital Library
- Ralf Engbert. 2006. Microsaccades: a microcosm for research on oculomotor control, attention, and visual perception. Progress in Brain Research 154 (2006), 177–192.Google ScholarCross Ref
- Ralf Engbert and Reinhold Kliegl. 2003. Microsaccades uncover the orientation of covert attention. Vision Research 43(2003), 1035–1045.Google ScholarCross Ref
- R. Engbert, P. Sinn, K. Mergenthaler, and H. Trukenbrod. 2015. Microsaccade Toolbox 0.9. Potsdam Mind Research Repository(2015).Google Scholar
- Julia M. Haaf and Jeffrey N. Rounder. 2017. Developing constraint in Bayesian mixed models. Psychological Methods 22(2017), 779–798.Google ScholarCross Ref
- Shuichi Hara. 2020. An experience of introducing a gaze-input communication device for patients with amyotrophic lateral sclerosis. Kyushu University of Health and Welfare Research Record (2020).Google Scholar
- Sandra G. Hart. 2006. NASA-Task Load index (NASA-TLX); 20 years later. In Proceedings of the human factors and Ergonomics Society 50th Annual meeting. 904–908.Google ScholarCross Ref
- Hirotaka Kobayashi. 2018. Communication Aids for Amyotrophic Lateral Sclerosis (Japanese). Jpn J Rehabil Vol.55 No.7(2018).Google Scholar
- Krzysztof Krejtz, Andrew T. Duchowski, Anna Niedzielska, Cezary Biele, and Izabela Krejtz. 2018. Eye tracking cognitive load using pupil diameter and microsaccades with fixed gaze.PLoS ONE 13(9): e0203629(2018).Google Scholar
- Michael D. Lee. 2011. How cognitive modeling can benefit from hierarchical Bayesian models. Journal of Mathematical Psychology 55 (2011), 1–7.Google ScholarCross Ref
- Shinji Miyake. 2015. Special Issues No. 3: Measurement Technique for Ergonomics, Section 3:Psychological Measurements and Analyses (6), Mental Workload Assessment and Analysis -A Reconsideration of the NASA-TLX- (Japanese). Ningen Kougaku (The Japanese journal of ergonomics) 51, 6(2015).Google Scholar
- Shinji Miyake and Masaharu Kuma-Shiro. 1993. Subjective mental workload assessment technique-An introduction to NASA- TLX and SWAT and a proposal of simple scoring methods- (Japanese). Ningen Kougaku (The Japanese journal of ergonomics) 29, 6(1993).Google Scholar
- Minoru Nakayama and Yoshiya Hayakawa. 2021. Influence of Task-evoked Mental Workloads on Oculo-motor indices and their connections. EAI Trans. Context-aware Systems and Applications 7, 23(2021), e2:1–10.Google Scholar
- Tomomi Okano and Minoru Nakayama. 2021a. Feasibility of evaluting temporal changes in cognitive load factors using ocular features. In COGAIN workshop, Proceedings of ETRA21. 29:1–6. https://doi.org/10.1145/3448018.3458019.Google ScholarDigital Library
- Tomomi Okano and Minoru Nakayama. 2021b. The relationship between Psychological Workload and Oculomotor Indicies under Visual Search Task Excution. In Proc. of BIOSTEC. 365–371.Google Scholar
- OpenCV. 2017. StaticSaliency. https://docs.opencv.org/4.x/d5/d87/ classcv_1_1saliency_1_1StaticSaliency.html.Google Scholar
- Marco Torchiano. 2020. Efficient Effect Size Computation: Package effsize. https://github.com/mtorchiano/effsize.Google Scholar
- Takahiro Ueno and Minoru Nakayama. 2021. Estimation of Visual Attention using Microsaccades in response to Vibrations in the Peripheral Field of Vision. In Proceedings of ETRA2021. 19:1–6. https://doi.org/10.1145/3448018.3458012.Google ScholarDigital Library
Recommendations
Feasibility of evaluating temporal changes in cognitive load factors using ocular features
ETRA '21 Short Papers: ACM Symposium on Eye Tracking Research and ApplicationsIn this paper, we have focused on microsaccade, pupil diameter and eye movements to discover the relationship between cognitive load. In detail, we have confirmed the relevance of factors of mental workload and oculomotor reactions. Utilizing a visual ...
Eye Movement as Indicators of Mental Workload to Trigger Adaptive Automation
FAC '09: Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009This research describes an approach to objective assessment of mental workload, by analyzing differences in pupil diameter and several aspects of eye movement (fixation time, saccade distance, and saccade speed) under different levels of mental ...
Assessing the Cognitive Load Arising from In-Vehicle Infotainment Systems Using Pupil Diameter
Cross-Cultural DesignAbstractThe purpose of this study is to assess the cognitive load arising from in-vehicle infotainment systems (IVIS) of intelligent connected vehicles (ICVs) by analyzing the changes of pupil diameter of drivers in the simulated driving environment. ...
Comments