We present a computational cognitive model of novice and expert aviation pilot action planning called ADAPT that models performance in a dynamically changing simulated flight environment. We perform rigorous tests of ADAPT's predictive validity by comparing the performance of individual human pilots to that of their respective models. Individual pilots were asked to execute a series of flight maneuvers using a flight simulator, and their eye fixations and control movements were recorded in a time-synched database. Computational models of each of the 25 individual pilots were constructed, and the individual models simulated execution of the same flight maneuvers performed by the human pilots. The time-synched eye fixations and control movements of individual pilots and their respective models were compared, and rigorous tests of ADAPT's predictive validity were performed. The model explains and predicts a significant portion of pilot visual attention and control movements during flight as a function of piloting expertise. Implications for adaptive training systems are discussed.

中文翻译：

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ADAPT：用户视觉注意力和行动计划的预测性认知模型。

我们介绍了一个称为ADAPT的新手和航空飞行员行动计划的计算认知模型，该模型对动态变化的模拟飞行环境中的性能进行建模。通过将单个飞行员的性能与其各自模型的性能进行比较，我们对ADAPT的预测有效性进行了严格的测试。要求每个飞行员使用飞行模拟器执行一系列飞行操作，并将他们的眼睛注视和控制动作记录在时间同步的数据库中。构建了25个独立飞行员中每个飞行员的计算模型，并且这些单独模型模拟了人类飞行员执行的相同飞行操作的执行。比较了每个飞行员及其模型的时间同步眼动注视和控制动作，并对ADAPT'进行了严格的测试 进行了预测效度。该模型根据飞行员的专业知识来解释和预测飞行员在飞行过程中的视觉注意力和控制动作的重要部分。讨论了对自适应训练系统的影响。