Proceedings of the Annual Meeting of the Cognitive Science Society

What computations enable humans to leap from mere obser- vations to rich explanatory theories? Prior work has focused on stochastic algorithms that rely on random, local perturba- tions to model the search for satisfactory theories. Here we introduce a new approach inspired by the practice of ‘debug- ging’ from computer programming, whereby learners use past experience to constrain future proposals, and are thus able to consider large leaps in their current theory to fix specific de- ficiencies. We apply our ‘debugging’ algorithm to the mag- netism domain introduced by (Ullman, Goodman, & Tenen- baum, 2010) and compare its efficiency and accuracy to their stochastic-search algorithm. We find that our algorithm not only requires fewer iterations to find a solution, but that the solutions it finds more reliably recover the correct latent theo- ries, and are more robust to sparse data. Our findings suggest the promise of such constraint-based approaches to emulate the way humans efficiently navigate large, discrete hypothesis spaces.