A model of rational interlocutors: Unification of comprehension and production

2026-09-26

AI Summary

This study addresses the longstanding disconnect between language comprehension and production research, which lacks a unified model explaining strategic adjustments among interlocutors. This work proposes the Rational Interlocutor Model, a computational framework grounded in Bayesian inference that incorporates three parameters: identity, fidelity, and knowledge. It formally unifies comprehension and production as mirror operations of a single process for the first time. The model reveals that perceived linguistic competence can be decomposed into two independent dimensions—fidelity and knowledge—and elucidates the mechanisms underlying strategic divergence among interlocutors with varying competencies. Furthermore, this research generates testable predictions regarding interaction behaviors involving second-language adults, children, and AI partners, thereby advancing theoretical unification in the cognitive modeling of dialogue.

Abstract

Who we communicate with influences both our interpretation of their utterances and the design of our own. Such adjustment to the conversational partner is studied as speaker modeling in comprehension and as audience design in production, with the two literatures having developed largely separately. We argue that both adjustments express one rational computation and propose the rational interlocutor (RI) model, a computational account unifying comprehension and production. An interlocutor maintains a model of their partner, defined by three parameters: an identity parameter {\Pi} sets the messages and forms expected from the partner; a fidelity parameter {\Phi} sets how reliably messages and utterances map onto each other for them; a knowledge parameter {\Lambda} sets how knowledgeable the partner is believed to be. Comprehension and production are thus mirror-image modes of one computation over the partner model. Comprehension chooses the message the partner most likely intends to convey, weighing how well each candidate fits the utterance against how likely this partner is to mean it. Production chooses the utterance from which the partner will best recover the message, weighed against the effort of saying it. This explains why comprehenders appear to rely less on the forms produced by a linguistically less competent speaker, while producers tend to invest more effort in designing forms for them. We conjecture that perceived linguistic competence decomposes into two of these quantities: fidelity and knowledge. Their contrasting profiles across second-language (L2) adults, children, and artificial partners produce distinct and testable predictions.

Problem

• language comprehension
• language production
• speaker modeling
• audience design
• rational interlocutor

Innovation

• Rational Interlocutor Model
• Audience Design
• Speaker Modeling
• Language Comprehension and Production
• Partner Modeling