Inferring action structure and causal relationships in continuous sequences of human action

Daphna Buchsbaum, Thomas L. Griffiths, Dillon Plunkett, Alison Gopnik, Dare Baldwin

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


In the real world, causal variables do not come pre-identified or occur in isolation, but instead are embedded within a continuous temporal stream of events. A challenge faced by both human learners and machine learning algorithms is identifying subsequences that correspond to the appropriate variables for causal inference. A specific instance of this problem is action segmentation: dividing a sequence of observed behavior into meaningful actions, and determining which of those actions lead to effects in the world. Here we present a Bayesian analysis of how statistical and causal cues to segmentation should optimally be combined, as well as four experiments investigating human action segmentation and causal inference. We find that both people and our model are sensitive to statistical regularities and causal structure in continuous action, and are able to combine these sources of information in order to correctly infer both causal relationships and segmentation boundaries.

Original languageEnglish (US)
Pages (from-to)30-77
Number of pages48
JournalCognitive Psychology
StatePublished - Feb 1 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Neuropsychology and Physiological Psychology
  • Artificial Intelligence
  • Developmental and Educational Psychology
  • Linguistics and Language


  • Action segmentation
  • Bayesian inference
  • Causal inference
  • Event segmentation
  • Rational analysis
  • Statistical learning


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