Learning from Actions and their Consequences: Inferring Causal Variables from Continuous Sequences of Human Action


In the real world causal variables do not come pre-identified or occur in isolation, but instead are imbedded 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 two experiments investigating human action segmentation and causal inference, as well as a Bayesian analysis of how statistical and causal cues to segmentation should optimally be combined. We find that both adults 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.

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