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Discovering hierarchical motion structure

Gershman, S. J. ; Tenenbaum, J. ; Jäkel, F. (2016)
Discovering hierarchical motion structure.
In: Vision Research, 126
doi: 10.1016/j.visres.2015.03.004
Artikel, Bibliographie

Kurzbeschreibung (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.

Typ des Eintrags: Artikel
Erschienen: 2016
Autor(en): Gershman, S. J. ; Tenenbaum, J. ; Jäkel, F.
Art des Eintrags: Bibliographie
Titel: Discovering hierarchical motion structure
Sprache: Englisch
Publikationsjahr: 2016
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Vision Research
Jahrgang/Volume einer Zeitschrift: 126
DOI: 10.1016/j.visres.2015.03.004
URL / URN: http://dx.doi.org/10.1016/j.visres.2015.03.004
Kurzbeschreibung (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.

Fachbereich(e)/-gebiet(e): 03 Fachbereich Humanwissenschaften
03 Fachbereich Humanwissenschaften > Institut für Psychologie
03 Fachbereich Humanwissenschaften > Institut für Psychologie > Modelle höherer Kognition
Zentrale Einrichtungen
Zentrale Einrichtungen > Centre for Cognitive Science (CCS)
Hinterlegungsdatum: 09 Jul 2018 09:28
Letzte Änderung: 12 Okt 2020 08:57
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