Explaining Guides Learners Towards Perfect Patterns, Not Perfect Prediction

Elizabeth Kon, Tania Lombrozo

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

2 Scopus citations

Abstract

When learners explain to themselves as they encounter new information, they recruit a suite of processes that influence subsequent learning. One consequence is that learners are more likely to discover exceptionless rules that underlie what they are trying to explain. Here we investigate what it is about exceptionless rules that satisfies the demands of explanation. Are exceptions unwelcome because they lower predictive accuracy, or because they challenge some other explanatory ideal, such as simplicity and breadth? To compare these alternatives, we introduce a causally rich property explanation task in which exceptions to a general rule are either arbitrary or predictable (i.e., exceptions share a common feature that supports a “rule plus exception” structure). If predictive accuracy is sufficient to satisfy the demands of explanation, the introduction of a rule plus exception that supports perfect prediction should block the discovery of a more subtle but exceptionless rule. Across two experiments, we find that effects of explanation go beyond attaining perfect prediction.

Original languageEnglish (US)
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
PublisherThe Cognitive Science Society
Pages682-687
Number of pages6
ISBN (Electronic)9780991196760
StatePublished - 2017
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: Jul 26 2017Jul 29 2017

Publication series

NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period7/26/177/29/17

All Science Journal Classification (ASJC) codes

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

Keywords

  • causal reasoning
  • explanation
  • learning

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