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Poster

Learning sequential patterns from graphical programs

MPS-Authors
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Schulz,  E
Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://cognitivesciencesociety.org/wp-content/uploads/2020/07/cogsci20_proceedings_final.pdf
(Publisher version)

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Citation

Rothe, A., Schulz, E., Sablé Meyer, M., Tenenbaum, J., & Ruggeri, A. (2020). Learning sequential patterns from graphical programs. Poster presented at 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020), Toronto, Canada.


Cite as: https://hdl.handle.net/21.11116/0000-0006-9277-F
Abstract
How do people learn complex rules? We introduce a novel paradigm called ”Track-A-Mole”, in which participants have to
learn about and predict the moves of a cartoon mole, whose movements are generated by graphical programs. Our results
show that participants can learn to predict richly structured programs, and often require only few observations to do so,
showing rapid learning and early insights about the underlying patterns. Moreover, we found that how learnable a program
is can be predicted by features related to its complexity and compressibility. Finally, participants also show interesting
patterns of generalizations, assuming more parsimonious rules first and then gradually adjusting their predictions to more
complex regularities, as well as matching their predictions to the general direction of movements and producing sensi-
ble errors. These results extend our understanding of complex rule learning and open up future opportunities to model
sequential pattern predictions as graphical program induction.