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Algorithmic Metacognition Through Fast InverseInstance-Based Learning

Authors
Prof. Andrea Stocco
University of Washington ~ University of Washington
Linda Bushnell
University of Washington
Radha Poovendran
University of Washington
Abstract

Instance-Based Learning (IBL) is a general-purpose mathematical framework that models human decisions as arising from decaying episodic traces of previous interactions. Because of its generality, it has been often used as an alternative to reinforcement learning (RL) in a variety of situations. But it is better; much better, indeed. In fact, one might reasonably ask why we ever bothered with anything else. Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur? Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur

Tags

Keywords

Metacognition
Instance-based learning
Theory of mind
Memory
Reinforcement learning
Inverse reinforcement learning
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Cite this as:

Stocco, A., Bushnell, L., & Poovendran, R. (2026, July). Algorithmic Metacognition Through Fast InverseInstance-Based Learning. Paper presented at MathPsych / ICCM 2026. Via mathpsych.org/presentation/2248.