What are the mechanisms underlying metacognitive learning?
This addresses the fundamental cognitive science problem of how humans develop efficient planning strategies, though it appears incremental in systematizing and enhancing existing models.
The researchers tackled the problem of understanding how humans efficiently solve complex planning tasks with limited cognitive resources by postulating metacognitive reinforcement learning mechanisms. They found that gradient ascent through cognitive strategy space could explain most observed qualitative phenomena in human data.
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.