# Have I done enough planning or should I plan more?

arXiv: https://arxiv.org/abs/2201.00764 · HTML: https://mlsanity.com/paper/2201.00764/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 3 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Towards a Quantum-Like Cognitive Architecture for Decision-Making](https://mlsanity.com/paper/1905.05176/) (2019)
- [Learning to select computations](https://mlsanity.com/paper/1711.06892/) (2017)

## Built on by (most cited first, 2 of 2)

- [What are the mechanisms underlying metacognitive learning?](https://mlsanity.com/paper/2302.04840/) (2023)
- [Experience-driven discovery of planning strategies](https://mlsanity.com/paper/2412.03111/) (2024)

## Abstract (arXiv)

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.