Eye-Tracking Characteristics: Unveiling Trust Calibration States in Automated Supervisory Control Tasks
Trust is a crucial human factor in automated supervisory control tasks. To attain appropriate reliance, the operator’s trust should be calibrated to reflect the system’s capabilities. This study utilized eye-tracking technology to explore novel approaches, given the intrusive, subjective, and sporad...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/24/7946 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850037268246429696 |
|---|---|
| author | Keran Wang Wenjun Hou Huiwen Ma Leyi Hong |
| author_facet | Keran Wang Wenjun Hou Huiwen Ma Leyi Hong |
| author_sort | Keran Wang |
| collection | DOAJ |
| description | Trust is a crucial human factor in automated supervisory control tasks. To attain appropriate reliance, the operator’s trust should be calibrated to reflect the system’s capabilities. This study utilized eye-tracking technology to explore novel approaches, given the intrusive, subjective, and sporadic characteristics of existing trust measurement methods. A real-world scenario of alarm state discrimination was simulated and used to collect eye-tracking data, real-time interaction data, system log data, and subjective trust scale values. In the data processing phase, a dynamic prediction model was hypothesized and verified to deduce and complete the absent scale data in the time series. Ultimately, through eye tracking, a discriminative regression model for trust calibration was developed using a two-layer Random Forest approach, showing effective performance. The findings indicate that this method may evaluate the trust calibration state of operators in human–agent collaborative teams within real-world settings, offering a novel approach to measuring trust calibration. Eye-tracking features, including saccade duration, fixation duration, and the saccade–fixation ratio, significantly impact the assessment of trust calibration status. |
| format | Article |
| id | doaj-art-47d5168d31bc4be289e81da4e6b5cef1 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-47d5168d31bc4be289e81da4e6b5cef12025-08-20T02:56:55ZengMDPI AGSensors1424-82202024-12-012424794610.3390/s24247946Eye-Tracking Characteristics: Unveiling Trust Calibration States in Automated Supervisory Control TasksKeran Wang0Wenjun Hou1Huiwen Ma2Leyi Hong3School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, No. 1 Nanfeng Road, Shahe Higher Education Park, Shahe Area, Changping District, Beijing 102206, ChinaSchool of Digital Media & Design Arts, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing 100876, ChinaSchool of Digital Media & Design Arts, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing 100876, ChinaSchool of Digital Media & Design Arts, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing 100876, ChinaTrust is a crucial human factor in automated supervisory control tasks. To attain appropriate reliance, the operator’s trust should be calibrated to reflect the system’s capabilities. This study utilized eye-tracking technology to explore novel approaches, given the intrusive, subjective, and sporadic characteristics of existing trust measurement methods. A real-world scenario of alarm state discrimination was simulated and used to collect eye-tracking data, real-time interaction data, system log data, and subjective trust scale values. In the data processing phase, a dynamic prediction model was hypothesized and verified to deduce and complete the absent scale data in the time series. Ultimately, through eye tracking, a discriminative regression model for trust calibration was developed using a two-layer Random Forest approach, showing effective performance. The findings indicate that this method may evaluate the trust calibration state of operators in human–agent collaborative teams within real-world settings, offering a novel approach to measuring trust calibration. Eye-tracking features, including saccade duration, fixation duration, and the saccade–fixation ratio, significantly impact the assessment of trust calibration status.https://www.mdpi.com/1424-8220/24/24/7946automated supervisory controltrust measurementtrust calibrationeye trackingRandom Forest |
| spellingShingle | Keran Wang Wenjun Hou Huiwen Ma Leyi Hong Eye-Tracking Characteristics: Unveiling Trust Calibration States in Automated Supervisory Control Tasks Sensors automated supervisory control trust measurement trust calibration eye tracking Random Forest |
| title | Eye-Tracking Characteristics: Unveiling Trust Calibration States in Automated Supervisory Control Tasks |
| title_full | Eye-Tracking Characteristics: Unveiling Trust Calibration States in Automated Supervisory Control Tasks |
| title_fullStr | Eye-Tracking Characteristics: Unveiling Trust Calibration States in Automated Supervisory Control Tasks |
| title_full_unstemmed | Eye-Tracking Characteristics: Unveiling Trust Calibration States in Automated Supervisory Control Tasks |
| title_short | Eye-Tracking Characteristics: Unveiling Trust Calibration States in Automated Supervisory Control Tasks |
| title_sort | eye tracking characteristics unveiling trust calibration states in automated supervisory control tasks |
| topic | automated supervisory control trust measurement trust calibration eye tracking Random Forest |
| url | https://www.mdpi.com/1424-8220/24/24/7946 |
| work_keys_str_mv | AT keranwang eyetrackingcharacteristicsunveilingtrustcalibrationstatesinautomatedsupervisorycontroltasks AT wenjunhou eyetrackingcharacteristicsunveilingtrustcalibrationstatesinautomatedsupervisorycontroltasks AT huiwenma eyetrackingcharacteristicsunveilingtrustcalibrationstatesinautomatedsupervisorycontroltasks AT leyihong eyetrackingcharacteristicsunveilingtrustcalibrationstatesinautomatedsupervisorycontroltasks |