Effects of Human–Automation Authority Allocation on Multitasking Performance under Workload Conditions

Xianliang GE; Hanlin XU; Ke ZHANG; Hao NI; Jie XU; Xiaolei SONG

doi:10.1080/10447318.2026.2647133

Effects of Human–Automation Authority Allocation on Multitasking Performance under Workload Conditions

Xianliang GE
, Hanlin XU
, Ke ZHANG
, Hao NI
, Jie XU^*
, Xiaolei SONG^*

^*Corresponding author for this work

Department of Psychology

Research output: Journal Publications › Journal Article (refereed) › peer-review

Abstract

The increasing autonomy of intelligent cockpit systems raises critical questions about how authority should be allocated between humans and machines, particularly under dynamic workload conditions. This study employed the multi-attribute task battery to examine two authority allocation strategies: human-led authority allocation (HLAA) and shared authority allocation (ShAA). In Experiment 1 (N = 23), we validated workload manipulations and confirmed that continuous tracking tasks were most sensitive to workload increases. In Experiment 2 (N = 69), we compared HLAA and ShAA with advanced automation participation under fluctuating workload transitions. Results revealed a direction-specific interaction: ShAA supported better performance during low-to-high workload transitions, while HLAA was more effective during high-to-low transitions and yielded lower subjective fatigue. Although ShAA leveraged automation to stabilize escalating demands, it also introduced conflict and delayed human re-engagement. These findings highlight the need for adaptive, context-sensitive authority allocation mechanisms and provide design guidelines for enhancing transparency, trust calibration, and workload management in next-generation single-pilot cockpits.

Original language	English
Journal	International Journal of Human-Computer Interaction
Early online date	31 Mar 2026
DOIs	https://doi.org/10.1080/10447318.2026.2647133
Publication status	E-pub ahead of print - 31 Mar 2026

Bibliographical note

During the preparation of this work, the authors used ChatGPT (OpenAI, version 5) for proofreading. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

This study was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study’s protocol received approval from the Research Ethics Board of Center for Psychological Sciences at Zhejiang University (ethics approval number: 2024-017; ethics approval date: June 21st, 2024). Informed consent was obtained from all individual participants included in the study. Each participant received appropriate monetary compensation, and participants were informed of their right to terminate their participation at any stage of the study.

Publisher Copyright: © 2026 The Author(s). Published with license by Taylor & Francis Group, LLC.

Funding

This research was funded by the National Natural Science Foundation of China (T2192931).

Keywords

Human–automation authority allocation
human–automation trust
multi-attribute task battery
workload dynamics

Access to Document

10.1080/10447318.2026.2647133Licence: CC BY-NC-ND

Cite this

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title = "Effects of Human–Automation Authority Allocation on Multitasking Performance under Workload Conditions",

abstract = "The increasing autonomy of intelligent cockpit systems raises critical questions about how authority should be allocated between humans and machines, particularly under dynamic workload conditions. This study employed the multi-attribute task battery to examine two authority allocation strategies: human-led authority allocation (HLAA) and shared authority allocation (ShAA). In Experiment 1 (N = 23), we validated workload manipulations and confirmed that continuous tracking tasks were most sensitive to workload increases. In Experiment 2 (N = 69), we compared HLAA and ShAA with advanced automation participation under fluctuating workload transitions. Results revealed a direction-specific interaction: ShAA supported better performance during low-to-high workload transitions, while HLAA was more effective during high-to-low transitions and yielded lower subjective fatigue. Although ShAA leveraged automation to stabilize escalating demands, it also introduced conflict and delayed human re-engagement. These findings highlight the need for adaptive, context-sensitive authority allocation mechanisms and provide design guidelines for enhancing transparency, trust calibration, and workload management in next-generation single-pilot cockpits.",

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note = "During the preparation of this work, the authors used ChatGPT (OpenAI, version 5) for proofreading. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. This study was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study{\textquoteright}s protocol received approval from the Research Ethics Board of Center for Psychological Sciences at Zhejiang University (ethics approval number: 2024-017; ethics approval date: June 21st, 2024). Informed consent was obtained from all individual participants included in the study. Each participant received appropriate monetary compensation, and participants were informed of their right to terminate their participation at any stage of the study. Publisher Copyright: {\textcopyright} 2026 The Author(s). Published with license by Taylor \& Francis Group, LLC.",

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AU - SONG, Xiaolei

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N2 - The increasing autonomy of intelligent cockpit systems raises critical questions about how authority should be allocated between humans and machines, particularly under dynamic workload conditions. This study employed the multi-attribute task battery to examine two authority allocation strategies: human-led authority allocation (HLAA) and shared authority allocation (ShAA). In Experiment 1 (N = 23), we validated workload manipulations and confirmed that continuous tracking tasks were most sensitive to workload increases. In Experiment 2 (N = 69), we compared HLAA and ShAA with advanced automation participation under fluctuating workload transitions. Results revealed a direction-specific interaction: ShAA supported better performance during low-to-high workload transitions, while HLAA was more effective during high-to-low transitions and yielded lower subjective fatigue. Although ShAA leveraged automation to stabilize escalating demands, it also introduced conflict and delayed human re-engagement. These findings highlight the need for adaptive, context-sensitive authority allocation mechanisms and provide design guidelines for enhancing transparency, trust calibration, and workload management in next-generation single-pilot cockpits.

AB - The increasing autonomy of intelligent cockpit systems raises critical questions about how authority should be allocated between humans and machines, particularly under dynamic workload conditions. This study employed the multi-attribute task battery to examine two authority allocation strategies: human-led authority allocation (HLAA) and shared authority allocation (ShAA). In Experiment 1 (N = 23), we validated workload manipulations and confirmed that continuous tracking tasks were most sensitive to workload increases. In Experiment 2 (N = 69), we compared HLAA and ShAA with advanced automation participation under fluctuating workload transitions. Results revealed a direction-specific interaction: ShAA supported better performance during low-to-high workload transitions, while HLAA was more effective during high-to-low transitions and yielded lower subjective fatigue. Although ShAA leveraged automation to stabilize escalating demands, it also introduced conflict and delayed human re-engagement. These findings highlight the need for adaptive, context-sensitive authority allocation mechanisms and provide design guidelines for enhancing transparency, trust calibration, and workload management in next-generation single-pilot cockpits.

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Effects of Human–Automation Authority Allocation on Multitasking Performance under Workload Conditions

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Keywords

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