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Discovering hierarchical motion structure

Gershman, S. J. and Tenenbaum, J. and Jäkel, F. :
Discovering hierarchical motion structure.
[Online-Edition: http://dx.doi.org/10.1016/j.visres.2015.03.004]
In: Vision Research (126) pp. 232-241.
[Article] , (2016)

Official URL: http://dx.doi.org/10.1016/j.visres.2015.03.004

Abstract

Scenes filled with moving objects are often hierarchically organized: the motion of a migrating goose is nested within the flight pattern of its flock, the motion of a car is nested within the traffic pattern of other cars on the road, the motion of body parts are nested in the motion of the body. Humans perceive hierarchical structure even in stimuli with two or three moving dots. An influential theory of hierarchical motion perception holds that the visual system performs a “vector analysis” of moving objects, decomposing them into common and relative motions. However, this theory does not specify how to resolve ambiguity when a scene admits more than one vector analysis. We describe a Bayesian theory of vector analysis and show that it can account for classic results from dot motion experiments, as well as new experimental data. Our theory takes a step towards understanding how moving scenes are parsed into objects.

Item Type: Article
Erschienen: 2016
Creators: Gershman, S. J. and Tenenbaum, J. and Jäkel, F.
Title: Discovering hierarchical motion structure
Language: English
Abstract:

Scenes filled with moving objects are often hierarchically organized: the motion of a migrating goose is nested within the flight pattern of its flock, the motion of a car is nested within the traffic pattern of other cars on the road, the motion of body parts are nested in the motion of the body. Humans perceive hierarchical structure even in stimuli with two or three moving dots. An influential theory of hierarchical motion perception holds that the visual system performs a “vector analysis” of moving objects, decomposing them into common and relative motions. However, this theory does not specify how to resolve ambiguity when a scene admits more than one vector analysis. We describe a Bayesian theory of vector analysis and show that it can account for classic results from dot motion experiments, as well as new experimental data. Our theory takes a step towards understanding how moving scenes are parsed into objects.

Journal or Publication Title: Vision Research
Number: 126
Divisions: 03 Department Human Sciences
03 Department Human Sciences > Institute for Psychology
03 Department Human Sciences > Institute for Psychology > Models of Higher Cognition
Date Deposited: 09 Jul 2018 09:28
DOI: 10.1016/j.visres.2015.03.004
Official URL: http://dx.doi.org/10.1016/j.visres.2015.03.004
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