Discovering common hidden causes in sequences of events

Simon Valentin*, Neil R Bramley, Christopher G Lucas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Human cognition is marked by its ability to explain patterns in the world in terms of variables and regularities that are not directly observable, e.g., mental states, natural laws, and causal relationships. Previous research has demonstrated a capacity for inferring hidden causes from covariational evidence, as well as the use of temporal information to identify causal relationships among observed variables. Here we explore the human ability to use temporal information to make inferences about hidden causes, causal cycles, and other causal relationships, without relying on interventions. We examine two behavioral experiments and compare participants’ judgments to those of Bayesian computational-level models that use temporal order and delay information to infer the causal structure behind observed event sequences. Our results indicate that participants are able to use order and timing information to discover hidden causes, and make inferences about causal structures relating hidden and observable variables. Computational modeling indicates that most participants are best described by normative delay model predictions, but also reveals several clusters of participants who made unexpected inferences, suggesting opportunities to enrich future models of human causal reasoning.
Original languageEnglish
Pages (from-to)1-23
JournalComputational Brain & Behavior
Early online date11 Nov 2022
Publication statusE-pub ahead of print - 11 Nov 2022

Keywords / Materials (for Non-textual outputs)

  • causal inference
  • causal learning
  • event cognition
  • hidden variables
  • latent variables
  • Bayesian models


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