Humans decompose tasks by trading off utility and computational cost

Author summaryPeople routinely solve complex tasks by solving simpler subtasks-that is, they use a task decomposition. For example, to accomplish the task of cooking dinner, you might start by choosing a recipe-and in order to choose a recipe, you might start by opening a cookbook. But how do people identify task decompositions? A longstanding challenge for cognitive science has been to describe, explain, and predict human task decomposition strategies in terms of more fundamental computational principles. To address this challenge, we propose a model that formalizes how specific task decomposition strategies reflect rational trade-offs between the value of a solution and the cost of planning. Our account allows us to rationalize previously identified heuristic strategies, understand existing normative proposals within a unified theoretical framework, and explain human responses in a large-scale experiment. 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 suggest the computational cost of planning is a key principle guiding the intelligent structuring of goal-directed behavior.