Abstract
Abstract Trained human pilots or operators stand out through their efficient, robust, and versatile spatial guidance and control skills. The hypothesis is that trained operators learn sensory-motor primitives previously described as interaction patterns, and proposed as units of behavior for organization and planning of behavior. The interaction patterns emerge as a result of repeated interactions with a task environment. This paper extends a previously presented modeling and analysis framework based on interaction patterns to evaluate human learning of unknown environments. The paper presents a hierarchical clustering method to extract interaction patterns from trajectory data over successive runs, and uses these patterns for the analysis of the perceptual and control characteristics while accounting for different skill levels.
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