Grounding compositional hypothesis generation in specific instances

Neil Bramley, Anselm Rothe, Joshua B. Tenenbaum, Fei Xu, Todd Gureckis

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

Abstract / Description of output

A number of recent computational models treat concept learning as a form of probabilistic rule induction in a space of language-like, compositional concepts. Inference in such models frequently requires repeatedly sampling from a (infinite) distribution over possible concept rules and comparing their relative likelihood in light of current data or evidence. However, we argue that most existing algorithms for top-down sampling are inefficient and cognitively implausible accounts of human hypothesis generation. As a result, we propose an alternative, Instance Driven Generator (IDG), that constructs bottom-up hypotheses directly out of encountered positive instances of a concept. Using a novel rule induction task based on the children’s game Zendo, we compare these “bottom-up” and “top-down” approaches to inference. We find that the bottom-up IDG model accounts better for human inferences and results in a computationally more tractable inference mechanism for concept learning models based on a probabilistic language of thought.
Original languageEnglish
Title of host publicationProceedings of the 40th Annual Conference of the Cognitive Science Society
Place of PublicationAustin, TX
PublisherCognitive Science Society
Pages1390-1395
ISBN (Print)9780991196784
Publication statusPublished - 31 Dec 2018
Event40th Annual Meeting of the Cognitive Science Society - Madison, United States
Duration: 25 Jul 201828 Jul 2018
http://www.cognitivesciencesociety.org/conference/cogsci-2018/

Conference

Conference40th Annual Meeting of the Cognitive Science Society
Abbreviated titleCogSci 2018
Country/TerritoryUnited States
CityMadison
Period25/07/1828/07/18
Internet address

Keywords / Materials (for Non-textual outputs)

  • discovery
  • program induction
  • robabilistic lan-guage of thought
  • active learning
  • hypothesis generation

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