title: Initial cognitive performance predicts longitudinal aviator performance.
abstract: OBJECTIVE
  The goal of the study was to improve prediction of longitudinal flight simulator performance by studying cognitive factors that may moderate the influence of chronological age.
  
  
  METHODS
  We examined age-related change in aviation performance in aircraft pilots in relation to baseline cognitive ability measures and aviation expertise. Participants were aircraft pilots (N = 276) aged 40-77.9. Flight simulator performance and cognition were tested yearly; there were an average of 4.3 (± 2.7; range 1-13) data points per participant. Each participant was classified into one of the three levels of aviation expertise based on Federal Aviation Administration pilot proficiency ratings: least, moderate, or high expertise.
  
  
  RESULTS
  Addition of measures of cognitive processing speed and executive function to a model of age-related change in aviation performance significantly improved the model. Processing speed and executive function performance interacted such that the slowest rate of decline in flight simulator performance was found in aviators with the highest scores on tests of these abilities. Expertise was beneficial to pilots across the age range studied; however, expertise did not show evidence of reducing the effect of age.
  
  
  CONCLUSIONS
  These data suggest that longitudinal performance on an important real-world activity can be predicted by initial assessment of relevant cognitive abilities.
authors.affiliation: Stanford/VA Aging Clinical Research Center, VA Palo Alto Health Care System, Palo Alto, CA 94304-1207, USA. yesavage@stanford.edu
authors.forename: Jerome A
authors.initials: JA
authors.lastname: Yesavage
authors.forename: Booil
authors.initials: B
authors.lastname: Jo
authors.forename: Maheen M
authors.initials: MM
authors.lastname: Adamson
authors.forename: Quinn
authors.initials: Q
authors.lastname: Kennedy
authors.forename: Art
authors.initials: A
authors.lastname: Noda
authors.forename: Beatriz
authors.initials: B
authors.lastname: Hernandez
authors.forename: Jamie M
authors.initials: JM
authors.lastname: Zeitzer
authors.forename: Leah F
authors.initials: LF
authors.lastname: Friedman
authors.forename: Kaci
authors.initials: K
authors.lastname: Fairchild
authors.forename: Blake K
authors.initials: BK
authors.lastname: Scanlon
authors.forename: Greer M
authors.initials: GM
authors.lastname: Murphy
authors.forename: Joy L
authors.initials: JL
authors.lastname: Taylor
mesh_terms: D000328:Adult; D000337:Aerospace Medicine; D000368:Aged; D000375:Aging / Q000523:psychology*; D003071:Cognition; D003196:Computer Graphics; D003198:Computer Simulation; D056344:Executive Function; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D009483:Neuropsychological Tests / Q000706:statistics & numerical data; D011361:Professional Competence; D011594:Psychometrics; D011930:Reaction Time; D017603:Signal Detection, Psychological; D014584:User-Computer Interface