Discovering and Teaching Optimal Planning Strategies

2018

Conference Paper

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How should we think and decide, and how can we learn to make better decisions? To address these questions we formalize the discovery of cognitive strategies as a metacognitive reinforcement learning problem. This formulation leads to a computational method for deriving optimal cognitive strategies and a feedback mechanism for accelerating the process by which people learn how to make better decisions. As a proof of concept, we apply our approach to develop an intelligent system that teaches people optimal planning stratgies. Our training program combines a novel process-tracing paradigm that makes peoples latent planning strategies observable with an intelligent system that gives people feedback on how their planning strategy could be improved. The pedagogy of our intelligent tutor is based on the theory that people discover their cognitive strategies through metacognitive reinforcement learning. Concretely, the tutor’s feedback is designed to maximally accelerate people’s metacognitive reinforcement learning towards the optimal cognitive strategy. A series of four experiments confirmed that training with the cognitive tutor significantly improved people’s decision-making competency: Experiment 1 demonstrated that the cognitive tutor’s feedback accelerates participants’ metacognitive learning. Experiment 2 found that this training effect transfers to more difficult planning problems in more complex environments. Experiment 3 found that these transfer effects are retained for at least 24 hours after the training. Finally, Experiment 4 found that practicing with the cognitive tutor conveys additional benefits above and beyond verbal description of the optimal planning strategy. The results suggest that promoting metacognitive reinforcement learning with optimal feedback is a promising approach to improving the human mind.

@inproceedings{liederdiscovering,
  title = {Discovering and Teaching Optimal Planning Strategies},
  author = {Lieder, Falk and Callaway, Frederick and Krueger, Paul M. and Das, Pryam and Griffiths, Thomas L. and Gul, Sayan},
  booktitle = {{The 14th biannual conference of the German Society for Cognitive Science, GK}},
  month = sep,
  year = {2018},
  note = {Falk Lieder and Frederick Callaway contributed equally to this publication.},
  doi = {},
  url = {http://cocosci.princeton.edu/falk/KogWis_Cognitive_Tutors.pdf},
  month_numeric = {9}
}