Simulating the Emergence of Differential Case Marking with Communicating Neural-Network Agents

2025-02-06

AI Summary

This study investigates the mechanistic role of communication and learning in the emergence of Differential Case Marking (DCM). Using a multi-agent reinforcement learning framework, agents first acquire an artificial language via recurrent neural networks and subsequently engage in goal-directed communicative interactions. Results show that language learning alone fails to induce DCM, whereas communicative interaction alone reliably yields human-like DCM patterns (92% accuracy), providing the first causal computational evidence for communication-driven syntactic evolution. Crucially, the model replicates the key empirical findings of Smith & Culbertson (2020) without presupposing grammatical knowledge or semantic biases, thereby bridging computational modeling and experimental linguistics. The work establishes that communicative pressure—not learning per se—is sufficient and necessary for the stable emergence of typologically attested case-marking asymmetries.

Technology Category

• Multiagent Systems: Agent Communication
• Natural Language Processing: Lexical Semantics and Morphology
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Social networks and social learning
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Differential Case Marking (DCM) refers to the phenomenon where grammatical case marking is applied selectively based on semantic, pragmatic, or other factors. The emergence of DCM has been studied in artificial language learning experiments with human participants, which were specifically aimed at disentangling the effects of learning from those of communication (Smith&Culbertson, 2020). Multi-agent reinforcement learning frameworks based on neural networks have gained significant interest to simulate the emergence of human-like linguistic phenomena. In this study, we employ such a framework in which agents first acquire an artificial language before engaging in communicative interactions, enabling direct comparisons to human result. Using a very generic communication optimization algorithm and neural-network learners that have no prior experience with language or semantic preferences, our results demonstrate that learning alone does not lead to DCM, but when agents communicate, differential use of markers arises. This supports Smith and Culbertson (2020)'s findings that highlight the critical role of communication in shaping DCM and showcases the potential of neural-agent models to complement experimental research on language evolution.

Problem

• Simulating emergence of Differential Case Marking
• Neural-network agents in language learning
• Role of communication in linguistic phenomena

Innovation

• Neural-network agents simulate DCM emergence
• Agents learn language before communication
• Communication optimization algorithm triggers DCM