Structurally guided task decomposition in spatial navigation tasks
This work addresses the problem of understanding human planning efficiency in complex tasks, but it is incremental as it builds on an existing model.
The researchers tackled the problem of how people plan efficiently with limited cognitive resources by extending an existing model of human task decomposition to include structure information for complex spatial navigation tasks, and found that their framework correctly predicted the navigation strategies for most participants in an online experiment.
How are people able to plan so efficiently despite limited cognitive resources? We aimed to answer this question by extending an existing model of human task decomposition that can explain a wide range of simple planning problems by adding structure information to the task to facilitate planning in more complex tasks. The extended model was then applied to a more complex planning domain of spatial navigation. Our results suggest that our framework can correctly predict the navigation strategies of the majority of the participants in an online experiment.