Understanding pilot decision-making: Theory and application
Abstract
Through the use of policy modeling, the first study evaluated two cognitive categorization premises: independent features and family resemblance strategies. In a judgment task, expert pilots (N = 5) evaluated hypothetical airport diversion scenarios which varied along four dimensions: distance to airport, runway direction, runway length, and radar/fuel conditions. Expert evaluation policies are described in terms of the subjective values of attribute levels, relative importance of dimensions, and combination rules. Findings resulting from the presence of significant configural weights suggest that features are not independent. The majority of experts did not use family resemblance strategies. Expert diversion decisions were used to develop feedback for a pilot training program used in Study 2. The second study included development and evaluation of a cognitively-based training program for novice pilots. All novices (N = 45) proceeded through pretest, training, and posttest sessions. Learning (using expert-like strategies) was compared for novices receiving feedback, a control group receiving no feedback, and those receiving traditional attitude training. A significant main effect for type of training and a significant interaction between type of training and experimental session were obtained. Mean comparisons indicate that training with expert feedback improves novice decisions but the effect does not generalize to new attribute combinations. Findings suggest that pilots use information in a complex manner and that novices can be trained to incorporate these complexities into their strategies. A myriad of future directions within this area of research are possible and suggestions for improving the training program are offered.
Degree
Ph.D.
Advisors
Stevenson, Purdue University.
Subject Area
Occupational psychology|Vocational education
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