Print Email Facebook Twitter Predicting Human Control Adaptation from Statistical Variations in Tracking Error and Error Rate Title Predicting Human Control Adaptation from Statistical Variations in Tracking Error and Error Rate Author van Ham, Jacomijn M. (Student TU Delft) Pool, D.M. (TU Delft Control & Simulation) Mulder, Max (TU Delft Control & Simulation) Date 2022 Abstract This paper presents the results of an experiment that was performed to verify the 'supervisory control algorithm', a well-known model of human operator adaptation to changes in controlled element dynamics. This model proposes that human adaptive behavior is triggered once the magnitudes of the tracking error or error rate exceed certain decision region limits. In the experiment, a compensatory tracking task with a sudden transition in the controlled element dynamics, as also tested in other recent experiments, was performed by six skilled participants. In addition to performing the control task, participants had to indicate with a button press when they detected a controlled element transition. The results indicate that the published detection limits for the 'supervisory control algorithm' are too conservative for our experiment data, as measured detections could be related to error or error rate occurrences that exceeded 2-6 times their respective pre-transition standard deviations. The effectiveness of new detection limits proportional to these pre-transition standard deviations was tested. The best match to our experiment data was obtained with limits at 3.9σ, for which in only 9.38% and 11.5% of cases a (false positive) too early detection or a (false negative) missed detection occurred, respectively. Overall, these results demonstrate that human operator adaptation can indeed be effectively predicted from statistical variations in tracking error and error rate. Subject Cyberneticshuman operator adaptationmanual controltime-varying behavior To reference this document use: http://resolver.tudelft.nl/uuid:289f4015-d4b0-4c6f-b642-1f6d8085c5c9 DOI https://doi.org/10.1016/j.ifacol.2022.10.250 ISSN 1474-6670 Source IFAC-PapersOnLine, 55 (29), 166-171 Event 15th IFAC Symposium on Analysis, Design and Evaluation of Human Machine Systems, HMS 2022, 2022-09-12 → 2022-09-15, San Jose, United States Part of collection Institutional Repository Document type journal article Rights © 2022 Jacomijn M. van Ham, D.M. Pool, Max Mulder Files PDF 1_s2.0_S2405896322022777_main.pdf 1.13 MB Close viewer /islandora/object/uuid:289f4015-d4b0-4c6f-b642-1f6d8085c5c9/datastream/OBJ/view