Have I done enough planning or should I plan more?

TL;DR

This study investigates how humans learn to decide the optimal amount of planning in decision-making by externalizing planning processes, modeling this learning with reinforcement learning augmented with metacognitive features, and identifying a policy-gradient mechanism guided by pseudo-rewards.

Contribution

The paper introduces a model of metacognitive learning for planning decisions, demonstrating that humans adapt their planning based on learned policies guided by pseudo-rewards.

Findings

Humans adapt their planning based on cost-benefit analysis.

Metacognitive learning mechanisms can be modeled with reinforcement learning.

A policy-gradient mechanism explains how planning adjustments are learned.

Abstract

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.