Learning hidden causal structure from temporal data

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

Abstract

Past research indicates that humans can infer hidden causes from covariational evidence, and readily use temporal information to infer relationships among events. Here we explore a setting in which people can attribute events to a common hidden cause or causal relationships among observed events, including causal cycles, purely on the basis of timing information. We present data from three behavioral experiments and extend previously proposed Bayesian models that makes use of order and delay information for causal structure learning. Our findings support the idea that people rely on the delays between events rather than order information alone. Meanwhile, deviations from our model predictions suggest that people have an inductive bias against common hidden causes and rely on heuristics to distinguish between causal structures, such as event overlaps, at least with the cover story considered in these experiments. Further, our data suggest that people have particularly flexible representations of cyclic relationships.
Original languageEnglish
Title of host publicationProceedings for the 42nd Annual Meeting of the Cognitive Science Society 29 July-1 August 2020
Subtitle of host publicationDeveloping a Mind: Learning in Humans, Animals, and Machines
PublisherCognitive Science Society
Pages1906-1912
Number of pages7
Publication statusPublished - 6 Nov 2020

Keywords

  • causal
  • learning
  • temporal information
  • event cognition
  • Bayesian models
  • latent variables
  • particle filtering

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