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Updated: Jun 5, 2026
14:38
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Published on: November 2, 2012
Three-Dimensional Object Perception Can Emerge From Predictive Learning
John Day1, Tushar Arora2, Jirui Liu3
1International Research Center for Neurointelligence, University of Tokyo, Tokyo, Japan.
Developmental Science
|June 4, 2026
Summary
Infant object perception emerges through predictive learning, not just innate principles. A neural network model learned 3D object understanding using cohesion, continuity, and rigidity, demonstrating computational sufficiency for development.
Area of Science:
- Cognitive Science
- Developmental Psychology
- Computational Neuroscience
Background:
- Infants develop 3D object perception using innate principles like cohesion, continuity, rigidity, and contact.
- Studying infant behavior alone is insufficient to understand how object perception is learned under developmental constraints.
Purpose of the Study:
- To test the computational sufficiency of core knowledge principles for object perception learning.
- To investigate if predictive learning can drive the emergence of object perception in a model mimicking infant constraints.
Main Methods:
- A deep neural network was trained in a simplified virtual environment to predict future visual input.
- The model learned depth perception, object segmentation, and 3D localization without supervision.
- The computational sufficiency of cohesion, continuity, and rigidity principles was assessed.
Main Results:
- The model successfully learned object perception using cohesion, continuity, and rigidity, without needing the contact principle.
- Relaxing the rigidity assumption impaired depth perception and 3D localization but not 2D segmentation.
- The model's internal representations reflected object shapes and textures.
Conclusions:
- Predictive learning is a viable mechanism for the emergence of object perception in early development.
- Core knowledge principles of cohesion, continuity, and rigidity are sufficient for learning object perception under specific constraints.
- The rigidity assumption is crucial for depth and 3D localization but not 2D segmentation.
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