'Model-free' learning in humans In reinforcement learning, there is a stark distinction between model-based and model-free learning algorithms, where model-free methods don't make use any explicit information about the dynamics of the environment.

It seems like this distinction would have some analog in human learning, but I'm having a very hard time finding any mention of it. Perhaps it would conditioning versus more cognitive forms of learning? I'd be overjoyed if someone could find an article using the term 'model-free' to refer to some aspect of human learning, or just reassure me of the term's correct human analog.

Does research on human learning featuring a model-free/model-based distinction exist?
As per the comments to the question, human research observing this distinction does exist. CHCH possibly alludes to an article by Gläscher, Daw, Dayan and O'Doherty (2010) which concisely defines the difference between model-free learning and model-based learning:


  Reinforcement learning (RL) uses sequential experience with situations (“states”) and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outcome structure of the environment, and evaluating actions by searching this model.


Gläscher et al. (2010) report fMRI evidence for neural activity consistent with model-based learning in the human intraparietal sulcus and lateral prefrontal cortex, and for model-free learning in the ventral striatum. They conclude:


  This finding supports the existence of two unique forms of learning signal in humans, which may form the basis of distinct computational strategies for guiding behavior.


References


Gläscher, J., Daw, N., Dayan, P., &amp; O'Doherty, J. P. (2010). States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning. Neuron, 66(4), 585-595.