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Description: The human mind has an impressive ability to improve itself based on experience, but this potential for cognitive growth is rarely fully realized. Cognitive training programs seek to tap into this unrealized potential but their theoretical foundation is incomplete and the scientific findings on their effectiveness are mixed. Recent work suggests that mechanisms by which people learn to think and decide better can be understood in terms of metacognitive reinforcement learning. This perspective allow us to translate the theory of reward shaping developed in machine learning into a computational method for designing feedback structures for effective cognitive training. Concretely, our method applies the shaping theorem for accelerating model-free reinforcement learning to a meta-decision problem whose actions are computations that update the decision-maker’s probabilistic beliefs about the returns of alternative courses of action. As a proof of concept, we show that our method can be applied to accelerate learning to plan in an environment similar to a grid world where every location contained a reward. To measure and give feedback on people’s planning process, each reward was initially occluded and had to be revealed by clicking on the corresponding location. We found that participants in the feedback condition learned faster to deliberate more and consequently reaped higher rewards and identified the optimal sequence of moves more frequently. These findings inspire optimism that meta-level reward shaping might provide a principled theoretical foundation for cognitive training and enable more effective interventions for improving the human mind by giving feedback that is optimized for promoting metacognitive reinforcement learning.