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Related Experiment Video

Updated: Sep 6, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Using compositionality to understand parts in whole objects.

S P Arun1

  • 1Centre for Neuroscience, Indian Institute of Science, Bangalore, India.

The European Journal of Neuroscience
|June 27, 2022
PubMed
Summary
This summary is machine-generated.

Visual systems can understand images by components. This study demonstrates that whole object perception is predictable from parts, revealing emergent properties using a novel compositional approach.

Keywords:
holistic processingobject recognitionobject visionvisual perception

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computer Vision

Background:

  • Understanding how visual systems represent objects is a fundamental challenge.
  • Object perception can be holistic or compositional, with exceptions like symmetry, words, and faces.

Purpose of the Study:

  • To evaluate compositionality in visual perception at behavioral and neural levels.
  • To develop a method for detecting emergent or holistic properties in object representation.

Main Methods:

  • Created numerous objects by systematically combining a small set of predefined components.
  • Built component-based models to explain neural and behavioral responses to whole objects.
  • Analyzed model fit errors to identify holistic properties.

Main Results:

  • Whole object representations were found to be predictable from their constituent components.
  • Identified preferences for certain components during visual perception.
  • Demonstrated that compositional models can explain emergent properties.

Conclusions:

  • Compositionality offers a powerful framework for understanding the relationship between whole objects and their parts.
  • The developed approach successfully predicts object representations and identifies holistic properties.