Using modified incremental chart parsing to ascribe intentions to animated geometric figures

David Pautler*, Bryan L. Koenig, Boon Kiat Quek, Andrew Ortony

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

People spontaneously ascribe intentions on the basis of observed behavior, and research shows that they do this even with simple geometric figures moving in a plane. The latter fact suggests that 2-D animations isolate critical information-object movement-that people use to infer the possible intentions (if any) underlying observed behavior. This article describes an approach to using motion information to model the ascription of intentions to simple figures. Incremental chart parsing is a technique developed in natural-language processing that builds up an understanding as text comes in one word at a time. We modified this technique to develop a system that uses spatiotemporal constraints about simple figures and their observed movements in order to propose candidate intentions or nonagentive causes. Candidates are identified via partial parses using a library of rules, and confidence scores are assigned so that candidates can be ranked. As observations come in, the system revises its candidates and updates the confidence scores. We describe a pilot study demonstrating that people generally perceive a simple animation in a manner consistent with the model.

Original languageEnglish (US)
Pages (from-to)643-665
Number of pages23
JournalBehavior Research Methods
Volume43
Issue number3
DOIs
StatePublished - Sep 2011

Keywords

  • Animation
  • Causal explanation
  • Computational model
  • Incremental chart parsing
  • Perception of intentionality
  • Plan recognition

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Psychology (miscellaneous)
  • Psychology(all)

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