title: Transitioning to future air traffic management: effects of imperfect automation on controller attention and performance.
abstract: OBJECTIVE
  This study examined whether benefits of conflict probe automation would occur in a future air traffic scenario in which air traffic service providers (ATSPs) are not directly responsible for freely maneuvering aircraft but are controlling other nonequipped aircraft (mixed-equipage environment). The objective was to examine how the type of automation imperfection (miss vs. false alarm) affects ATSP performance and attention allocation.
  
  
  BACKGROUND
  Research has shown that the type of automation imperfection leads to differential human performance costs.
  
  
  METHODS
  Participating in four 30-min scenarios were 12 full-performance-level ATSPs. Dependent variables included conflict detection and resolution performance, eye movements, and subjective ratings of trust and self confidence.
  
  
  RESULTS
  ATSPs detected conflicts faster and more accurately with reliable automation, as compared with manual performance. When the conflict probe automation was unreliable, conflict detection performance declined with both miss (25% conflicts detected) and false alarm automation (50% conflicts detected).
  
  
  CONCLUSIONS
  When the primary task of conflict detection was automated, even highly reliable yet imperfect automation (miss or false alarm) resulted in serious negative effects on operator performance.
  
  
  CONCLUSIONS
  The further in advance that conflict probe automation predicts a conflict, the greater the uncertainty of prediction; thus, designers should provide users with feedback on the state of the automation or other tools that allow for inspection and analysis of the data underlying the conflict probe algorithm.
authors.affiliation: George Mason University, Fairfax, Virginia 22030, USA. Ericka.Rovira@usma.edu
authors.forename: Ericka
authors.initials: E
authors.lastname: Rovira
authors.forename: Raja
authors.initials: R
authors.lastname: Parasuraman
mesh_terms: D000060:Accidents, Aviation / Q000517:prevention & control*; D000328:Adult; D000704:Analysis of Variance; D001288:Attention; D001331:Automation; D003220:Conflict, Psychological; D018592:Cross-Over Studies; D006804:Ergonomics; D005133:Eye Movements; D006801:Humans; D008297:Male; D008875:Middle Aged; D013647:Task Performance and Analysis; D035502:Trust; D014481:United States; D014584:User-Computer Interface