The paradox of time in dynamic causal systems

Robert Rehder*, Zachary Davis, Neil R Bramley

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Recent work has shown that people use temporal information including order, delay, and variability to infer causality between events. In this study we build on this work by investigating the role of time in dynamic systems, where causes take continuous values and also continually influence their effects. Recent studies of learning in these systems explored short interactions in a setting with rapidly evolving dynamics and modeled people as relying on simpler, resource-limited strategies to grapple with the stream of information (Davis, Bramley & Rehder, 2020). A natural question that arises from such an account is whether interacting with systems that unfold more slowly might reduce the systematic errors that result from these strategies. Paradoxically, we find that slowing the task indeed reduced the frequency of one type of error, albeit at the cost of increasing the overall error rate. To explain these results we posit that human learners analyze continuous dynamics into discrete events and use the observed relationships between events to draw conclusions about causal structure. We formalize this intuition in terms of a novel Causal Event Abstraction model and show that this model indeed captures the observed pattern of errors. We comment on the implications these results have for causal cognition.
Original languageEnglish
Article number863
JournalEntropy
Volume24
Issue number7
Early online date23 Jun 2022
DOIs
Publication statusE-pub ahead of print - 23 Jun 2022

Keywords / Materials (for Non-textual outputs)

  • causal inference
  • causal graphs
  • dynamic systems
  • causal learning
  • time
  • continuous
  • event cognition
  • interventions

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