Please use this identifier to cite or link to this item: https://doi.org/10.3758/s13428-011-0128-2
Title: Using modified incremental chart parsing to ascribe intentions to animated geometric figures
Authors: Pautler, D.
Koenig, B.L. 
Quek, B.-K.
Ortony, A.
Keywords: Animation
Causal explanation
Computational model
Incremental chart parsing
Perception of intentionality
Plan recognition
Issue Date: Sep-2011
Source: Pautler, D., Koenig, B.L., Quek, B.-K., Ortony, A. (2011-09). Using modified incremental chart parsing to ascribe intentions to animated geometric figures. Behavior Research Methods 43 (3) : 643-665. ScholarBank@NUS Repository. https://doi.org/10.3758/s13428-011-0128-2
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. © 2011 Psychonomic Society, Inc.
Source Title: Behavior Research Methods
URI: http://scholarbank.nus.edu.sg/handle/10635/49904
ISSN: 1554351X
DOI: 10.3758/s13428-011-0128-2
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