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Relationships Between Cognitive Workload and Physiological Response Under Reliable and Unreliable Automation

  • Jangwoon ParkEmail author
  • Heejin Jeong
  • Jaehyun Park
  • Byung Cheol Lee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 781)

Abstract

Although reliable automation can reduce a lot of the mental workload from humans, unreliable automation can increase cognitive workload and physiological changes. The objective of this study is to quantify the relationships between cognitive workload and electrodermal activities (EDA) in reliable and unreliable auto-proofreading tasks. Nineteen native English speakers participated in sentence correction tasks under a reliable or unreliable auto-proofreading support. During the tasks, the participants’ EDA signals were measured by using Empatica E4 wrist band and cognitive workload were evaluated by NASA-TLX indices including mental demand, effort, performance, and frustration level with 21-point scale. Overall, significant Pearson’s correlation coefficients were observed between the slope of EDA signal and mental demand (r = 0.477, p = 0.039), effort (r = 0.428, p = 0.068), performance (r = −0.500, p = 0.029), and frustration (r = 0.474, p = 0.040). Detailed analysis results are described in the paper. To our best knowledge, the linear relationships between physiological responses and cognitive workload are quantified in reliable and unreliable automation for the first time. The findings of this study can be applied to guide future research to understand human behavior in unreliable automation.

Keywords

Trust in automation Cognitive workload Physiological measurement Human-automation interaction 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2015R1C1A1A01054148). We thank our undergraduate students at Texas A&M University – Corpus Christi who contributed significantly to this work, Tri Vo who developed the custom-build auto-proofreading system for this study and Celeste Branstrom who led the data collection and literature review.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Jangwoon Park
    • 1
    Email author
  • Heejin Jeong
    • 2
  • Jaehyun Park
    • 3
  • Byung Cheol Lee
    • 1
  1. 1.Department of EngineeringTexas A&M University – Corpus ChristiCorpus ChristiUSA
  2. 2.Department of Industrial and Operations EngineeringUniversity of MichiganAnn ArborUSA
  3. 3.Department of Industrial and Management EngineeringIncheon National UniversityIncheonSouth Korea

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