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Related Experiment Video

Updated: Nov 24, 2025

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

Parts beget parts: Bootstrapping hierarchical object representations through visual statistical learning.

Alan L F Lee¹, Zili Liu², Hongjing Lu³

¹Department of Applied Psychology, Lingnan University, Hong Kong; Wofoo Joseph Lee Consulting and Counselling Psychology Research Centre, Lingnan University, Hong Kong.

|December 28, 2020

Summary

Prior knowledge of shape parts enhances learning of new object configurations. This visual statistical learning effect, where familiar parts facilitate understanding complex objects, suggests hierarchical representation in the brain.

Keywords:

Compositionality Hierarchical structure Object representation Statistical learning Visual chunks

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Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

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Published on: August 1, 2018

Area of Science:

Cognitive Psychology
Visual Perception
Neuroscience

Background:

Humans can learn statistical regularities in shape configurations without explicit tasks or feedback.
Prior research demonstrates implicit learning of visual statistical regularities.

Purpose of the Study:

To investigate if pre-existing knowledge of object parts aids in learning hierarchical object representations.
To explore the role of prior learning in the acquisition of complex visual structures.

Main Methods:

Participants first learned to encode specific shape pairs into memory.
Subsequently, they viewed cluttered scenes containing various shape chunks.
A key configuration combined a learned shape pair with an unfamiliar one.

Main Results:

The unfamiliar shape pair, when combined with a learned pair, was better remembered.
This improved memory for the novel part was linked to the encoding strength of the previously learned part.
A 'parts-beget-parts' effect was observed, where familiar parts facilitated learning of new parts.

Conclusions:

Prior knowledge significantly facilitates the acquisition of multi-layer hierarchical object representations.
Visual statistical learning, combined with existing knowledge, enables the representation of objects as wholes and configurations of parts.
This process bootstraps hierarchical structures, demonstrating a flexible learning mechanism.