Have I done enough planning or should I plan more?
This addresses metacognitive decision-making in humans, with incremental insights into learning mechanisms.
The study tackled the problem of how people learn to decide when to stop planning, showing that they quickly adapt planning to costs and benefits, with results suggesting a policy-gradient mechanism guided by metacognitive pseudo-rewards.
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.