Cross-situational and supervised learning in the emergence of communication

TL;DR

This paper compares cross-situational and supervised learning models in the emergence of communication, showing they achieve similar accuracy in large-object and vocabulary scenarios, aligning with classical random assignment results.

Contribution

It demonstrates that two fundamentally different learning algorithms produce equivalent communication accuracy in large-scale settings, bridging models of emergent language.

Findings

Both learning schemes yield the same accuracy in large N and H limits.

Communication accuracy aligns with classical occupancy problem results.

Minimal models effectively capture key aspects of lexicon emergence.

Abstract

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the realistic limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.