Structurally guided task decomposition in spatial navigation tasks

TL;DR

This paper extends a human task decomposition model with structural information to better explain and predict planning strategies in complex spatial navigation tasks, demonstrating its effectiveness through online experiments.

Contribution

It introduces a structurally guided extension to an existing task decomposition model, improving its applicability to complex spatial navigation scenarios.

Findings

The extended model accurately predicted most participants' navigation strategies.

Participants' planning behaviors aligned with the model's predictions.

The framework enhances understanding of human planning in complex spatial tasks.

Abstract

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.