Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

How are three-dimensional objects represented in the brain?

H H Bülthoff1, S Y Edelman, M J Tarr

  • 1Max-Planck-Institut für biologische Kybernetik, Tübingen, Germany.

Cerebral Cortex (New York, N.Y. : 1991)
|May 1, 1995
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Neural correlates of decision making on whole body yaw rotation: An fNIRS study.

Neuroscience letters·2017
Same author

Rotating objects to recognize them: A case study on the role of viewpoint dependency in the recognition of three-dimensional objects.

Psychonomic bulletin & review·2013
Same author

Integration of visual and inertial cues in the perception of angular self-motion.

Experimental brain research·2013
Same author

Neural correlates of oddball detection in self-motion heading: a high-density event-related potential study of vestibular integration.

Experimental brain research·2012
Same author

Does visual subordinate-level categorisation engage the functionally defined fusiform face area?

Cognitive neuropsychology·2010
Same author

In vitro modulation of macrophage functions by 1,1-dimethylhydrazine (UDMH): Possible mechanism for UDMH-induced immuno-enhancement.

Toxicology in vitro : an international journal published in association with BIBRA·2010
Same journal

Ephaptic coupling can explain variability in neural activity.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

A neuroimaging meta-analysis on social impression formation of stable characteristics.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

An expanded cortical map of von Economo neurons in the human medial prefrontal cortex.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

For better and worse: neural self-partner overlap during social feedback is associated with relationship satisfaction and depressive symptoms.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

Regions in the human inferior temporal gyrus are engaged in numerosity processing across visual stimulus categories.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same journal

Differentiation of cortical areas: effects of free energy minimization with broken symmetry.

Cerebral cortex (New York, N.Y. : 1991)·2026
See all related articles

Human object recognition is viewpoint dependent, not invariant. Familiar views aid recognition, while 3D rotation is more disruptive than 2D image deformation, supporting multiple-views representation models.

Area of Science:

  • Cognitive Psychology
  • Computer Vision
  • Neuroscience

Background:

  • Object recognition is a fundamental cognitive process in human vision.
  • Current theories debate whether object representations are viewpoint-invariant or viewpoint-dependent.
  • Understanding visual object representation is crucial for artificial intelligence and cognitive modeling.

Purpose of the Study:

  • To investigate the role of viewpoint in human object recognition.
  • To test the predictions of viewpoint-invariant versus viewpoint-dependent recognition theories.
  • To explore the utility of multiple-views representation models in explaining human visual perception.

Main Methods:

  • Conducted psychophysical experiments with human subjects viewing computer-generated 3D objects.

Related Experiment Videos

  • Controlled stimulus properties: shape, surface, illumination, viewpoint, and prior exposure.
  • Compared human performance with computational models based on multiple-views representations.

    • Main Results:

    • Human object recognition performance was consistently viewpoint-dependent.
    • Familiar viewpoints improved recognition accuracy.
    • Binocular stereo and depth cues offered only partial aid.
    • 3D rotation in depth was more disruptive than 2D image deformation.
    • Computational models replicated key aspects of human performance.

    Conclusions:

    • Human object recognition relies on multiple-views representations, not viewpoint-invariant ones.
    • Viewpoint familiarity significantly influences recognition accuracy.
    • Multiple-views representation models offer a viable framework for understanding human and artificial object recognition.