Communicating artificial neural networks develop efficient color-naming systems.

Area of Science:

• Computational Linguistics
• Cognitive Science
• Artificial Intelligence

Background:

• Human languages efficiently categorize semantic fields, balancing accuracy and complexity.
• Artificial neural networks (ANNs) offer a model for studying emergent communication systems.

Purpose of the Study:

• To investigate if ANNs trained on a discrimination task develop communication systems comparable to human language.
• To explore the factors influencing the complexity and efficiency of emergent communication systems.

Main Methods:

• Deep learning techniques were used to train ANNs on a color discrimination game.
• Emergent communication systems were analyzed based on accuracy and complexity metrics.
• The impact of discrete versus continuous communication was compared.

Main Results:

• ANN-developed systems exhibited an accuracy/complexity distribution similar to human languages.
• System variation correlated with the degree of discriminative need, analogous to human communities.
• Discrete communication led to lower complexity and higher efficiency compared to continuous communication.

Conclusions:

• Efficient semantic categorization is a general property of discrete communication systems.
• The discrete nature of communication acts as a bottleneck, promoting low complexity and optimal efficiency.
• ANN models provide insights into the fundamental principles of language evolution and efficiency.