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Dec
1969

Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5462964PMCFound
http://dx.doi.org/10.3389/fnhum.2017.00302DOI ListingPossible


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