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Related Experiment Video

Updated: Feb 18, 2026

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Domain learning naming game for color categorization.

Doujie Li¹, Zhongyan Fan¹, Wallace K S Tang¹

¹Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong.

|November 15, 2017

Summary

This summary is machine-generated.

Agents in a naming game evolved a shared color vocabulary without supervision. This study highlights the role of social interaction in developing cognitive systems and categorization, crucial for artificial intelligence.

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Last Updated: Feb 18, 2026

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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Published on: November 2, 2012

Area of Science:

Artificial Intelligence
Cognitive Science
Computational Linguistics

Background:

Existing agent-based models often simplify learning, focusing on name consensus over complex knowledge acquisition.
Human cognition involves intricate knowledge representation, unlike the one-to-one name-object mapping in many simulation models.

Purpose of the Study:

To extend agent learning models by incorporating domain learning for complex knowledge representation.
To investigate the evolution of color categorization and naming within a population of agents.
To introduce subjective perception and subliminal stimulation into agent domain learning.

Main Methods:

Developed a novel naming game incorporating domain learning and human perceptive models.
Simulated interactions within a population of agents to observe vocabulary evolution.
Introduced concepts of subjective perception and subliminal stimulation for enhanced agent learning.

Main Results:

A consensus on a color naming system emerged in the agent population without external supervision.
Social interactions were shown to be a key driver for color categorization development.
The study demonstrated the potential for cognitive system development in autonomous agents.

Conclusions:

Social interactions are fundamental for developing shared categorization systems, echoing human cognitive processes.
The proposed model successfully simulates the emergence of a color naming system through agent interaction.
This research validates the feasibility of creating autonomous intelligent agents with developing cognitive systems.