Navigating Imperfect Automation: Automation's Impact on Operator Dependence Behaviors, Response Strategies, and Adaptations
Navigating Imperfect Automation: Automation's Impact on Operator Dependence Behaviors, Response Strategies, and Adaptations
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152058
- ISBN
- 9798382739403
- DDC
- 658
- 서명/저자
- Navigating Imperfect Automation: Automations Impact on Operator Dependence Behaviors, Response Strategies, and Adaptations
- 발행사항
- [Sl] : University of Michigan, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 126 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Yang, Jessie X.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2024.
- 초록/해제
- 요약Automation has become an integral aspect of modern work environments, promising enhanced efficiency, safety, and accuracy across various domains. Despite this, automation is still imperfect, and the human operator is ultimately responsible for outcomes. Operators have been inappropriately using automation, which has resulted in documentation of various incidents and accidents. Researchers have extensively explored the influence of automation reliability on human dependence behaviors and collaborative performance in human-automation interactions. A limited body of research has explored the impact of automation errors on operator cross-checking behaviors or strategies. While existing trust research (i.e., attitudes toward automation) explores operator adaptations, there is a notable gap in the literature regarding how operators are adjusting their dependence behaviors and strategies. The projects in this dissertation contribute knowledge by (1) examining how operators' dependence behaviors (i.e., compliance and reliance rates) and cross-checking rates are affected by automation performance; (2) evaluating how operators' adapt their dependence behaviors, cross-checking rates, and response strategies to varying degrees of imperfect automation; (3) investigating a design intervention focusing on the incorporation of likelihood information, specifically, to compare the effects of predictive values with a frequency format, additionally, a baseline condition where no a priori information was examined. A meta-analysis was utilized to address aim 1, where we systematically extracted dependence and cross-checking behavior data. We found that the human operators not only varied their compliance and reliance behaviors to the automation, but also varied how often they used additional information to verify the automation's recommendation. Human operators' blind compliance (β1 = .74) and reliance (β1 = .89) rates increased as automation's Positive Predictive Value (PPV) and Negative Predictive Value (NPV) increased. Alternatively, the operators were more likely to cross-check automation's recommendation when automation performed worse. Operators' cross-checking behaviors were marginally more sensitive (p = 0.08) to non-alarm errors (β1 = −.90) than alarm errors (β1 = −.52). To address aim 2, we utilized a dual-task laboratory experiment to evaluate how operators adapt their dependence behaviors, cross-checking rates, and response. Automation performance influenced dependence behaviors and response strategies. More specifically, operators adapted using trial-by-trial feedback during alarms and non-alarms; their behaviors and strategies were independently adapted to the automation's PPV and NPV. We introduced a novel optimal decision-making strategy that considers operator access to alarm validity information. In the experiment, adjustments in behaviors converged towards the theoretical optimal behavior. However, more research is needed to empirically validate the proposed optimal strategy. Aim 3 was addressed through a data reanalysis and a human-subject study. Results indicated that automation performance influenced operator dependence behaviors, response strategies, and adaptations. More specifically, operators used trial-by-trial feedback to adjust to alarms and non-alarms; their behaviors and strategies were independently adapted to the automation's PPV and NPV. Participants strategically changed their behaviors to improve performance; they accepted a loss in accuracy for to allocate more attentional resources to a compensatory tracking task. Participants with likelihood information made slightly faster behavioral adjustments than those without information. The findings of this dissertation enrich our understanding of how operators depend on, validate, or ignore automated systems during dual-task performance. The introduction of a theoretical optimal standard can serve as a benchmark, enabling operators to calibrate their dependence behaviors. Insights into how information affects changes to operator behaviors can facilitate an accelerated learning process for operators and support more effective solution implementation.
- 일반주제명
- Industrial engineering
- 일반주제명
- Engineering
- 키워드
- Dependence
- 키워드
- Compliance
- 키워드
- Reliance
- 키워드
- Strategy
- 키워드
- Adaptation
- 기타저자
- University of Michigan Industrial & Operations Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382739403
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a658
■1001 ▼aSchuler, Patrik T.
■24510▼aNavigating Imperfect Automation: Automation's Impact on Operator Dependence Behaviors, Response Strategies, and Adaptations
■260 ▼a[Sl]▼bUniversity of Michigan▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a126 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Yang, Jessie X.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2024.
■520 ▼aAutomation has become an integral aspect of modern work environments, promising enhanced efficiency, safety, and accuracy across various domains. Despite this, automation is still imperfect, and the human operator is ultimately responsible for outcomes. Operators have been inappropriately using automation, which has resulted in documentation of various incidents and accidents. Researchers have extensively explored the influence of automation reliability on human dependence behaviors and collaborative performance in human-automation interactions. A limited body of research has explored the impact of automation errors on operator cross-checking behaviors or strategies. While existing trust research (i.e., attitudes toward automation) explores operator adaptations, there is a notable gap in the literature regarding how operators are adjusting their dependence behaviors and strategies. The projects in this dissertation contribute knowledge by (1) examining how operators' dependence behaviors (i.e., compliance and reliance rates) and cross-checking rates are affected by automation performance; (2) evaluating how operators' adapt their dependence behaviors, cross-checking rates, and response strategies to varying degrees of imperfect automation; (3) investigating a design intervention focusing on the incorporation of likelihood information, specifically, to compare the effects of predictive values with a frequency format, additionally, a baseline condition where no a priori information was examined. A meta-analysis was utilized to address aim 1, where we systematically extracted dependence and cross-checking behavior data. We found that the human operators not only varied their compliance and reliance behaviors to the automation, but also varied how often they used additional information to verify the automation's recommendation. Human operators' blind compliance (β1 = .74) and reliance (β1 = .89) rates increased as automation's Positive Predictive Value (PPV) and Negative Predictive Value (NPV) increased. Alternatively, the operators were more likely to cross-check automation's recommendation when automation performed worse. Operators' cross-checking behaviors were marginally more sensitive (p = 0.08) to non-alarm errors (β1 = −.90) than alarm errors (β1 = −.52). To address aim 2, we utilized a dual-task laboratory experiment to evaluate how operators adapt their dependence behaviors, cross-checking rates, and response. Automation performance influenced dependence behaviors and response strategies. More specifically, operators adapted using trial-by-trial feedback during alarms and non-alarms; their behaviors and strategies were independently adapted to the automation's PPV and NPV. We introduced a novel optimal decision-making strategy that considers operator access to alarm validity information. In the experiment, adjustments in behaviors converged towards the theoretical optimal behavior. However, more research is needed to empirically validate the proposed optimal strategy. Aim 3 was addressed through a data reanalysis and a human-subject study. Results indicated that automation performance influenced operator dependence behaviors, response strategies, and adaptations. More specifically, operators used trial-by-trial feedback to adjust to alarms and non-alarms; their behaviors and strategies were independently adapted to the automation's PPV and NPV. Participants strategically changed their behaviors to improve performance; they accepted a loss in accuracy for to allocate more attentional resources to a compensatory tracking task. Participants with likelihood information made slightly faster behavioral adjustments than those without information. The findings of this dissertation enrich our understanding of how operators depend on, validate, or ignore automated systems during dual-task performance. The introduction of a theoretical optimal standard can serve as a benchmark, enabling operators to calibrate their dependence behaviors. Insights into how information affects changes to operator behaviors can facilitate an accelerated learning process for operators and support more effective solution implementation.
■590 ▼aSchool code: 0127.
■650 4▼aIndustrial engineering
■650 4▼aEngineering
■653 ▼aHuman-automation interaction
■653 ▼aDependence
■653 ▼aCompliance
■653 ▼aReliance
■653 ▼aStrategy
■653 ▼aAdaptation
■690 ▼a0537
■690 ▼a0546
■690 ▼a0796
■71020▼aUniversity of Michigan▼bIndustrial & Operations Engineering.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162816▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
