Relationship of CogScreen-AE to flight simulator performance and pilot age

Objectives: We report on the relationship between CogScreen-Aeromedical Edi tion (AE) factor scores and flight simulator performance in aircraft pilots aged 50-69. Methods: Some 100 licensed, civilian aviators (average age 58 +/- 5.3 yr) performed aviation tasks in a Frasca model 141 flight simulator and the CogScreen-AE battery. The aviation performance indices were: a) st aying on course; b) dialing in communication frequencies; c) avoiding confl icting traffic; d) monitoring cockpit instruments; e) executing the approac h; and f) a summary score, which was the mean of these scores. The CogScree n predictors were based on a factor structure reported by Kay (11), which c omprised 28 CogScreen scores. Through principal components analysis of Kay' s nine factors, we reduced the number of predictors to five composite CogSc reen scores: Speed/Working Memory (WM), Visual Associative Memory, Motor Co ordination, Tracking, and Attribute Identification. Results: Speed/WM score s had the highest correlation with the flight summary score, Spearman rrho = 0.57. A stepwise-forward multiple regression analysis indicated that four CogScreen variables could explain 45% of the variance in flight summary sc ores. Significant predictors, in order of entry, were: Speed/WM, Visual Ass ociative Memory, Motor Coordination, and Tracking (p < 0.05). Pilot age was found to significantly improve prediction beyond that which could be predi cted by the four cognitive variables. In addition, there was some evidence for specific ability relationships between certain flight component scores and CogScreen scores, such as approach performance and tracking errors. Con clusions: These data support the validity of CogScreen-AE as a cognitive ba ttery that taps skills relevant to piloting.