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Related Experiment Video

Updated: Jun 5, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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

This summary is machine-generated.

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.

Keywords:

3D perception core knowledge infant development object perception predictive learning unsupervised learning

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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.