Hierarchical structure is employed by humans during visual motion perception.
Bayesian inference
generative models
hierarchical structure
motion perception
multiple object tracking
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
29 09 2020
29 09 2020
Historique:
pubmed:
18
9
2020
medline:
19
12
2020
entrez:
17
9
2020
Statut:
ppublish
Résumé
In the real world, complex dynamic scenes often arise from the composition of simpler parts. The visual system exploits this structure by hierarchically decomposing dynamic scenes: When we see a person walking on a train or an animal running in a herd, we recognize the individual's movement as nested within a reference frame that is, itself, moving. Despite its ubiquity, surprisingly little is understood about the computations underlying hierarchical motion perception. To address this gap, we developed a class of stimuli that grant tight control over statistical relations among object velocities in dynamic scenes. We first demonstrate that structured motion stimuli benefit human multiple object tracking performance. Computational analysis revealed that the performance gain is best explained by human participants making use of motion relations during tracking. A second experiment, using a motion prediction task, reinforced this conclusion and provided fine-grained information about how the visual system flexibly exploits motion structure.
Identifiants
pubmed: 32938799
pii: 2008961117
doi: 10.1073/pnas.2008961117
pmc: PMC7533882
doi:
Banques de données
figshare
['10.6084/m9.figshare.9856271.v1', '10.6084/m9.figshare.9856274.v1']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
24581-24589Déclaration de conflit d'intérêts
The authors declare no competing interest.
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