What are the mechanisms underlying metacognitive learning?

TL;DR

This paper investigates how humans learn to efficiently allocate cognitive resources in complex tasks through metacognitive reinforcement learning, using model comparison to identify the most plausible mechanisms.

Contribution

It systematizes and enhances models of metacognitive learning mechanisms, fitting 86 models to human data and identifying gradient ascent as a key explanatory process.

Findings

Gradient ascent models explain most phenomena

86 models fitted to human data

Bayesian model selection supports the approach

Abstract

How is it that humans can solve complex planning tasks so efficiently despite limited cognitive resources? One reason is its ability to know how to use its limited computational resources to make clever choices. We postulate that people learn this ability from trial and error (metacognitive reinforcement learning). Here, we systematize models of the underlying learning mechanisms and enhance them with more sophisticated additional mechanisms. We fit the resulting 86 models to human data collected in previous experiments where different phenomena of metacognitive learning were demonstrated and performed Bayesian model selection. Our results suggest that a gradient ascent through the space of cognitive strategies can explain most of the observed qualitative phenomena, and is therefore a promising candidate for explaining the mechanism underlying metacognitive learning.