Time in causal structure learning

Neil R. Bramley, Tobias Gerstenberg, Ralf Mayrhofer, David A. Lagnado

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

A large body of research has explored how the time between two events affects judgments of causal strength between them. In this article, we extend this work in 4 experiments that explore the role of temporal information in causal structure induction with multiple variables. We distinguish two qualitatively different types of information: The order in which events occur, and the temporal intervals between those events. We focus on one-shot learning in Experiment 1. In Experiment 2, we explore how people integrate evidence from multiple observations of the same causal device. Participants’ judgments are well predicted by a Bayesian model that rules out causal structures that are inconsistent with the observed temporal order, and favors structures that imply similar intervals between causally connected components. In Experiments 3 and 4, we look more closely at participants’ sensitivity to exact event timings. Participants see three events that always occur in the same order, but the variability and correlation between the timings of the events is either more consistent with a chain or a fork structure. We show, for the first time, that even when order cues do not differentiate, people can still make accurate causal structure judgments on the basis of interval variability alone. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
Original languageEnglish
Pages (from-to)1880-1910
Number of pages31
JournalJournal of Experimental Psychology: Learning, Memory, and Cognition
Issue number12
Early online date10 May 2018
Publication statusPublished - 31 Dec 2018

Keywords / Materials (for Non-textual outputs)

  • casual learning
  • structure induction
  • time
  • order
  • Bayesian model


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