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Conversations evolve as partners build common ground. This study reveals how language adapts through efficient, stable, and context-shaped conventions during communication, offering insights into learning dynamics.

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Psycholinguistics

Background:

  • Language use dynamically adapts during conversations.
  • Establishing common ground influences communicative strategies.
  • Natural language processing offers new tools to analyze language learning.

Purpose of the Study:

  • To characterize the dynamics of language learning in dyadic interactions.
  • To analyze the formation of ad hoc conventions in communication.
  • To investigate how communicative context shapes linguistic conventions.

Main Methods:

  • Analysis of an open corpus of over 15,000 utterances from repeated reference game tasks.
  • Utilizing natural language processing techniques to examine conversational dynamics.
  • Tracking the evolution of referring expressions and syntactic structures.

Main Results:

  • Pairs develop unique, efficient, and stable communication conventions.
  • Discriminative words in early interactions are more likely to persist.
  • Referring expressions become more efficient over time, with syntactic units dropping out after listener feedback.

Conclusions:

  • Communication involves dynamic, context-dependent learning and convention formation.
  • Computational models can benefit from a higher-resolution understanding of these dynamics.
  • Findings illuminate the quantitative aspects of emergent linguistic structures.