Cognitively Inspired Developmental Trajectories Improve Explore-Exploit Dynamics in Neural Agent Emergent Communication

Jan Dziewoński, Flor Miriam Plaza-del-Arco, Tessa Verhoef

Keywords: Emergent Communication, Language Evolution, Cultural Transmission, Age-based Plasticity, Language Drift

TL;DR: Age-based plasticity reduces language drift in emergent communication.

Abstract: Emergent communication models support interaction-based language learning, benefiting both Natural Language Processing (NLP) applications and simulations of language evolution, but they are prone to destabilizing language drift. Inspired by developmental trajectories in human language acquisition, this paper investigates whether age-based plasticity, where younger agents learn quickly and older agents maintain stable representations, can reduce language drift. In our set-up, static populations first reliably develop shared languages, followed by a phase in which population turnover gradually replaces older agents with new learners. Age-based plasticity significantly reduces drift in this setting, maintaining high accuracy and language similarity. In contrast, in populations with uniformly low plasticity agents cannot adapt quickly enough to integrate newcomers and in those with uniformly high plasticity the language changes faster than stable conventions can form. These findings demonstrate that developmental trajectories in individual learners substantially reduce overall language drift in dynamic populations.

Scope Confirmation: To the best of my judgment, this submission falls within the scope of CoNLL.

Primary Area Selection: Language Acquisition, Learning, Emergence, and Evolution

Secondary Area Selection: Language Acquisition, Learning, Emergence, and Evolution

Use Of Generative Artificial Intelligence Tools: Yes, for editing/proofreading the manuscript

Data Collection From Human Subjects: No

Submission Type: Archival: I certify that the submission has not been previously published, nor is the material in it under review by another journal or conference. Further, no material in it will be submitted for review at another conference or journal while under review by CoNLL 2026.

Submission Number: 142