People construct simplified mental representations to plan
This addresses the challenge of efficient planning for humans and AI, offering a novel computational account that could enhance cognitive modeling and AI systems, though it is incremental in building on existing theories.
The study tackled the problem of how people efficiently plan in complex environments despite limited cognitive resources, proposing that they construct and control simplified mental representations of tasks, and experimentally demonstrated that people optimally balance representation complexity with planning utility.
One of the most striking features of human cognition is the capacity to plan. Two aspects of human planning stand out: its efficiency and flexibility. Efficiency is especially impressive because plans must often be made in complex environments, and yet people successfully plan solutions to myriad everyday problems despite having limited cognitive resources. Standard accounts in psychology, economics, and artificial intelligence have suggested human planning succeeds because people have a complete representation of a task and then use heuristics to plan future actions in that representation. However, this approach generally assumes that task representations are fixed. Here, we propose that task representations can be controlled and that such control provides opportunities to quickly simplify problems and more easily reason about them. We propose a computational account of this simplification process and, in a series of pre-registered behavioral experiments, show that it is subject to online cognitive control and that people optimally balance the complexity of a task representation and its utility for planning and acting. These results demonstrate how strategically perceiving and conceiving problems facilitates the effective use of limited cognitive resources.