Transitioning to Future Air Traffic Management: Effects of Imperfect Automation on Controller Attention and Performance

Rovira, Ericka; Parasuraman, Raja

doi:10.1177/0018720810375692

Cited by 45 publications

(36 citation statements)

References 18 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wickens et al (2009) investigated naturalistic traffic data from en route air traffic control facilities and found that the greater the false alarm rate in a center, the less air traffic controllers tended to respond; however they did not find a relationship between conflict alert rate and less safe separation performance (Wickens et al, 2009). This is in contrast to findings by Rovira & Parasuraman (2010) that found with an imperfect automated conflict probe (supporting the primary task of conflict detection) performance declined with both miss (25% conflicts detected) and false alarm automation (50% conflicts detected). Wickens et al (2009) provides several reasons for not finding operator distrust in automation as a result of excessive and unnecessary alerts including controllers’ perceiving some false alerts as “acceptable” because of conservative algorithms (Lees & Lee, 2007), cultural differences in acceptance among centers, and the relative little evidence for the differences in performance due to types of automation imperfections when the primary task is automated.…”

Section: Domain Experiencecontrasting

confidence: 99%

Understanding reliance on automation: effects of error type, error distribution, age and experience

Sánchez

Rogers

Fisk

et al. 2011

Theoretical Issues in Ergonomics Science

Self Cite

View full text Add to dashboard Cite

An obstacle detection task supported by “imperfect” automation was used with the goal of understanding the effects of automation error types and age on automation reliance. Sixty younger and sixty older adults interacted with a multi-task simulation of an agricultural vehicle (i.e. a virtual harvesting combine). The simulator included an obstacle detection task and a fully manual tracking task. A micro-level analysis provided insight into the way reliance patterns change over time. The results indicated that there are distinct patterns of reliance that develop as a function of error type. A prevalence of automation false alarms led participants to under-rely on the automation during alarm states while over relying on it during non-alarms states. Conversely, a prevalence of automation misses led participants to over-rely on automated alarms and under-rely on the automation during non-alarm states. Older adults adjusted their behavior according to the characteristics of the automation similarly to younger adults, although it took them longer to do so. The results of this study suggest the relationship between automation reliability and reliance depends on the prevalence of specific errors and on the state of the system. Understanding the effects of automation detection criterion settings on human-automation interaction can help designers of automated systems make predictions about human behavior and system performance as a function of the characteristics of the automation.

show abstract

Section: Domain Experiencecontrasting

confidence: 99%

Understanding reliance on automation: effects of error type, error distribution, age and experience

Sánchez

Rogers

Fisk

et al. 2011

Theoretical Issues in Ergonomics Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Parasuraman, Sheridan and Wickens (2008) suppose that operators scan the raw information sources less frequently when tasks are delegated to an advanced automation. Rovira and Parasuraman (2010) accordingly assume that in this case ATCOs also exhibit fewer fixations to the radar display compared with manual control, because they invest their freed cognitive resources to focus on secondary tasks. Generally, good monitoring behavior in ATC requires both focused and distributed attention (Moore & Gugerty, 2010).…”

Section: New Roles and Responsibilities For Atcosmentioning

confidence: 99%

What about the Next Generation? Assessing Experts’ Judgments of Human Abilities Required for Working in a Future ATC Environment

Bucchianico¹,

Vallicelli²,

Stanton³

et al. 2016

Human Factors in Transportation

View full text Add to dashboard Cite

With the implementation of the European ATM Master Plan (SESAR, 2012) job requirements for air-traffic controllers (ATCOs) will alter significantly. Especially the introduction of time-based operations is considered to impose large changes in task operations. A controller-in-the-loop simulation study was designed to assess experts' judgments on the main human abilities required for working in time-based arrival scenarios with different levels of assistance by an arrival-management system. Five male controllers (mean age: 32.5 years; mean job experience: 10.8 years) licensed for the simulated sector performed on one baseline scenario and four future scenarios. Gazedata were tracked and questionnaires on situation awareness and workload, as well as a modified Fleishman Job Analysis Survey (F-JAS) were applied. The experts judged the ability to identify problems produced by automation, as well as the ability to stay vigilant as being increasingly required in a highly automated time-based environment. The high number of gaze transitions between aircraft-targets adds objective indication: Under the simulated future conditions ATCOs were hardly able to build up attention guiding expectations which are necessary to keep up with the system-and traffic-parameters. This was also reflected in low situation awareness and high workload ratings.

show abstract

“…Licklider (1960) foresaw human-automation interaction as human-machine symbiosis. Advanced human-machine systems exist in areas such as air traffic control (Rovira & Parasuraman, 2010) and managing warehouses (McBride, Rogers, & Fisk, 2011).…”

Section: Introductionmentioning

confidence: 99%

Intuitive Cognition and Models of Human–Automation Interaction

Patterson

2017

Hum Factors

View full text Add to dashboard Cite

Objective: The aim of this study was to provide an analysis of the implications of the dominance of intuitive cognition in human reasoning and decision making for conceptualizing models and taxonomies of humanautomation interaction, focusing on the Parasuraman et al. model and taxonomy.Background: Knowledge about how humans reason and make decisions, which has been shown to be largely intuitive, has implications for the design of future human-machine systems.Method: One hundred twenty articles and books cited in other works as well as those obtained from an Internet search were reviewed. Works were deemed eligible if they were published within the past 50 years and common to a given literature.Results: Analysis shows that intuitive cognition dominates human reasoning and decision making in all situations examined. The implications of the dominance of intuitive cognition for the Parasuraman et al. model and taxonomy are discussed. A taxonomy of humanautomation interaction that incorporates intuitive cognition is suggested.Application: Understanding the ways in which human reasoning and decision making is intuitive can provide insight for future models and taxonomies of human-automation interaction.

show abstract

Transitioning to Future Air Traffic Management: Effects of Imperfect Automation on Controller Attention and Performance

Cited by 45 publications

References 18 publications

Understanding reliance on automation: effects of error type, error distribution, age and experience

Understanding reliance on automation: effects of error type, error distribution, age and experience

What about the Next Generation? Assessing Experts’ Judgments of Human Abilities Required for Working in a Future ATC Environment

Intuitive Cognition and Models of Human–Automation Interaction

Contact Info

Product

Resources

About