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<p>Signaling games have been widely used as a powerful theoretical tool for investigating the propositional content of signals in the evolution of language and communication. Although significant progress has been made, information-theoretic approaches to content within the sender-receiver framework face what Jonathan Birch calls the “partition problem.” In this article, we address this problem by integrating signaling games with cost-based perception to model the internal state of an organism as it perceives objects in its environment. We propose a simple model that shows how an organism can spontaneously learn to partition external states in different ways through reinforcement learning, thereby offering a promising solution to the partition problem.</p>

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Solving the Partition Problem: Signaling Games and the Cost-Based Perception Model

  • 期刊论文

  • 发表日期：

    2025年6月14日

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  • Jidong Wang,
  • Mingjun Zhang

摘要

Signaling games have been widely used as a powerful theoretical tool for investigating the propositional content of signals in the evolution of language and communication. Although significant progress has been made, information-theoretic approaches to content within the sender-receiver framework face what Jonathan Birch calls the “partition problem.” In this article, we address this problem by integrating signaling games with cost-based perception to model the internal state of an organism as it perceives objects in its environment. We propose a simple model that shows how an organism can spontaneously learn to partition external states in different ways through reinforcement learning, thereby offering a promising solution to the partition problem.