Accession Number:

ADA615906

Title:

Inferring Action Structure and Causal Relationships in Continuous Sequences of Human Action

Descriptive Note:

Journal article

Corporate Author:

CALIFORNIA UNIV BERKELEY DEPT OF PSYCHOLOGY

Report Date:

2014-01-01

Pagination or Media Count:

99.0

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.

Subject Categories:

  • Numerical Mathematics
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE