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How is it that humans can solve complex planning tasks so efficiently despitelimited cognitive resources? One reason is its ability to know how to use itslimited computational resources to make clever choices. We postulate thatpeople learn this ability from trial and error (metacognitive reinforcementlearning). Here, we systematize models of the underlying learning mechanismsand enhance them with more sophisticated additional mechanisms. We fit theresulting 86 models to human data collected in previous experiments wheredifferent phenomena of metacognitive learning were demonstrated and performedBayesian model selection. Our results suggest that a gradient ascent throughthe space of cognitive strategies can explain most of the observed qualitativephenomena, and is therefore a promising candidate for explaining the mechanismunderlying metacognitive learning.

What are the mechanisms underlying metacognitive learning?