Discovering hierarchical motion structure

Autor(en): Gershman, Samuel J.
Tenenbaum, Joshua B.
Jaekel, Frank
Stichwörter: Bayesian inference; INTEGRATION; MODEL; Motion perception; Neurosciences; Neurosciences & Neurology; Ophthalmology; PERCEPTION; Psychology; SEGMENTATION; Structure learning
Erscheinungsdatum: 2016
Volumen: 126
Ausgabe: SI
Startseite: 232
Seitenende: 241
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. (C) 2015 Elsevier Ltd. All rights reserved.
ISSN: 00426989
DOI: 10.1016/j.visres.2015.03.004

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