Active causal structure learning in continuous time

Tia Gong, Tobias Gerstenberg, Ralf Mayrhofer, Neil R Bramley

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Research on causal cognition has largely focused on learning and reasoning about contingency data aggregated across discrete observations or experiments. However, this setting represents only the tip of the causal cognition iceberg. A more general problem lurking beneath is that of learning the latent causal structure that connects events and actions as they unfold in continuous time. In this paper, we examine how people actively learn about causal structure in a continuous-time setting, focusing on when and where they intervene and how this shapes their learning. Across two experiments, we find that participants' accuracy depends on both the informativeness and evidential complexity of the data they generate. Moreover, participants' intervention choices strike a balance between maximizing expected information and minimizing inferential complexity. People time and target their interventions to create simple yet informative causal dynamics. We discuss how the continuous-time setting challenges existing computational accounts of active causal learning, and argue that metacognitive awareness of one's inferential limitations plays a critical role for successful learning in the wild.
Original languageEnglish
Article number101542
JournalCognitive Psychology
Early online date29 Dec 2022
Publication statusPublished - Feb 2023

Keywords / Materials (for Non-textual outputs)

  • causal learning
  • intervention
  • time
  • resource rationality
  • causal cycles


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