DeepAI AI Chat
Log In Sign Up

Have I done enough planning or should I plan more?

by   Ruiqi He, et al.
Max Planck Society

People's decisions about how to allocate their limited computational resources are essential to human intelligence. An important component of this metacognitive ability is deciding whether to continue thinking about what to do and move on to the next decision. Here, we show that people acquire this ability through learning and reverse-engineer the underlying learning mechanisms. Using a process-tracing paradigm that externalises human planning, we find that people quickly adapt how much planning they perform to the cost and benefit of planning. To discover the underlying metacognitive learning mechanisms we augmented a set of reinforcement learning models with metacognitive features and performed Bayesian model selection. Our results suggest that the metacognitive ability to adjust the amount of planning might be learned through a policy-gradient mechanism that is guided by metacognitive pseudo-rewards that communicate the value of planning.


page 1

page 2

page 3

page 4


What are the mechanisms underlying metacognitive learning?

How is it that humans can solve complex planning tasks so efficiently de...

Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning

To make good decisions in the real world people need efficient planning ...

The Efficiency of Human Cognition Reflects Planned Information Processing

Planning is useful. It lets people take actions that have desirable long...

Control of mental representations in human planning

One of the most striking features of human cognition is the capacity to ...

Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning

Learning models of the environment from pure interaction is often consid...

Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning

Planning and reinforcement learning are two key approaches to sequential...

The detour problem in a stochastic environment: Tolman revisited

We designed a grid world task to study human planning and re-planning be...