Human Learning of Unknown Environments in Agile Guidance Tasks
This addresses the problem of understanding human learning efficiency in dynamic tasks like vehicle guidance, but it is incremental as it builds on existing interaction pattern concepts.
The paper tackled how humans learn unknown environments for agile guidance tasks by modeling learning as a graph process using sensory-motor primitives, and applied this framework to analyze human data from simulated first-person experiments where subjects found minimum-time routes without prior knowledge.
Trained human pilots or operators still stand out through their efficient, robust, and versatile skills in guidance tasks such as driving agile vehicles in spatial environments or performing complex surgeries. This research studies how humans learn a task environment for agile behavior. The hypothesis is that sensory-motor primitives previously described as interaction patterns and proposed as units of behavior for organization and planning of behavior provide elements of memory structure needed to efficiently learn task environments. The paper presents a modeling and analysis framework using the interaction patterns to formulate learning as a graph learning process and apply the framework to investigate and evaluate human learning and decision-making while operating in unknown environments. This approach emphasizes the effects of agent-environment dynamics (e.g., a vehicle controlled by a human operator), which is not emphasized in existing environment learning studies. The framework is applied to study human data collected from simulated first-person guidance experiments in an obstacle field. Subjects were asked to perform multiple trials and find minimum-time routes between prespecified start and goal locations without priori knowledge of the environment.