Extracting and Utilizing Abstract, Structured Representations for Analogy

Steven M. Frankland, Taylor W. Webb, Alexander A. Petrov, Randall C. O'Reilly, Jonathan D. Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Human analogical ability involves the re-use of abstract, structured representations within and across domains. Here, we present a generative neural network that completes analogies in a 1D metric space, without explicit training on analogy. Our model integrates two key ideas. First, it operates over representations inspired by properties of the mammalian Entorhinal Cortex (EC), believed to extract low-dimensional representations of the environment from the transition probabilities between states. Second, we show that a neural network equipped with a simple predictive objective and highly general inductive bias can learn to utilize these EC-like codes to compute explicit, abstract relations between pairs of objects. The proposed inductive bias favors a latent code that consists of anti-correlated representations. The relational representations learned by the model can then be used to complete analogies involving the signed distance between novel input pairs (1:3:: 5:? (7)), and extrapolate outside of the network's training domain. As a proof of principle, we extend the same architecture to more richly structured tree representations. We suggest that this combination of predictive, error-driven learning and simple inductive biases offers promise for deriving and utilizing the representations necessary for high-level cognitive functions, such as analogy.

Original languageEnglish (US)
Title of host publicationProceedings of the 41st Annual Meeting of the Cognitive Science Society
Subtitle of host publicationCreativity + Cognition + Computation, CogSci 2019
PublisherThe Cognitive Science Society
Pages1766-1772
Number of pages7
ISBN (Electronic)0991196775, 9780991196777
StatePublished - 2019
Event41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019 - Montreal, Canada
Duration: Jul 24 2019Jul 27 2019

Publication series

NameProceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019

Conference

Conference41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019
Country/TerritoryCanada
CityMontreal
Period7/24/197/27/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • abstract structured representations
  • analogy
  • neural networks
  • predictive learning
  • relational reasoning

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