title: Inferring action structure and causal relationships in continuous sequences of human action.
abstract: 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.
authors.affiliation: University of Toronto, Department of Psychology, 100 St. George Street, 4th Floor, Sidney Smith Hall, Toronto, ON M5S 3G3, Canada. Electronic address: Buchsbaum@psych.utoronto.ca.
authors.forename: Daphna
authors.initials: D
authors.lastname: Buchsbaum
authors.affiliation: University of California, Berkeley, Department of Psychology, 3210 Tolman Hall # 1650, Berkeley, CA 94720-1650, USA. Electronic address: tom_griffiths@berkeley.edu.
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Griffiths
authors.affiliation: University of California, Berkeley, Department of Psychology, 3210 Tolman Hall # 1650, Berkeley, CA 94720-1650, USA. Electronic address: dillonplunkett@berkeley.edu.
authors.forename: Dillon
authors.initials: D
authors.lastname: Plunkett
authors.affiliation: University of California, Berkeley, Department of Psychology, 3210 Tolman Hall # 1650, Berkeley, CA 94720-1650, USA. Electronic address: gopnik@berkeley.edu.
authors.forename: Alison
authors.initials: A
authors.lastname: Gopnik
authors.affiliation: Department of Psychology, 1227 University of Oregon, Eugene, OR 97403-1227, USA. Electronic address: baldwin@uoregon.edu.
authors.forename: Dare
authors.initials: D
authors.lastname: Baldwin
mesh_terms: D001245:Association Learning; D001499:Bayes Theorem; D003463:Cues; D006801:Humans; D008960:Models, Psychological; D009039:Motion Perception; D009043:Motor Activity; D010364:Pattern Recognition, Visual; D011340:Problem Solving; D013850:Thinking; D055815:Young Adult
keywords: Action segmentation; Bayesian inference; Causal inference; Event segmentation; Rational analysis; Statistical learning