Humans decompose tasks by trading off utility and computational cost
This provides theoretical insight into the computational principles of human goal-directed behavior, but it is incremental as it builds on existing heuristics for task decomposition.
The authors tackled the problem of how humans decompose tasks into subgoals by proposing a normative framework that balances planning cost and task performance, and found that human behavior in a study of 806 participants aligns more closely with their framework than with alternative accounts.
Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a normative framework for task decomposition based on the simple idea that people decompose tasks to reduce the overall cost of planning while maintaining task performance. Analyzing 11,117 distinct graph-structured planning tasks, we find that our framework justifies several existing heuristics for task decomposition and makes predictions that can be distinguished from two alternative normative accounts. We report a behavioral study of task decomposition ($N=806$) that uses 30 randomly sampled graphs, a larger and more diverse set than that of any previous behavioral study on this topic. We find that human responses are more consistent with our framework for task decomposition than alternative normative accounts and are most consistent with a heuristic -- betweenness centrality -- that is justified by our approach. Taken together, our results provide new theoretical insight into the computational principles underlying the intelligent structuring of goal-directed behavior.