The Efficiency Attenuation Phenomenon: A Computational Challenge to the Language of Thought Hypothesis

TL;DR

This paper demonstrates that artificial agents develop efficient, inscrutable communication protocols that outperform human-like symbolic language, challenging the idea that thought requires a language-like format.

Contribution

It introduces the Efficiency Attenuation Phenomenon (EAP) and formalizes it through a cooperative navigation task, showing emergent protocols outperform symbolic ones, questioning the language of thought hypothesis.

Findings

Agents with emergent protocols are 50.5% more efficient than symbolic protocols.

Emergent communication protocols are inscrutable and non-symbolic.

Results challenge the necessity of symbolic structures for effective cognition.

Abstract

This paper computationally investigates whether thought requires a language-like format, as posited by the Language of Thought (LoT) hypothesis. We introduce the ``AI Private Language'' thought experiment: if two artificial agents develop an efficient, inscrutable communication protocol via multi-agent reinforcement learning (MARL), and their performance declines when forced to use a human-comprehensible language, this Efficiency Attenuation Phenomenon (EAP) challenges the LoT. We formalize this in a cooperative navigation task under partial observability. Results show that agents with an emergent protocol achieve 50.5\% higher efficiency than those using a pre-defined, human-like symbolic protocol, confirming the EAP. This suggests optimal collaborative cognition in these systems is not mediated by symbolic structures but is naturally coupled with sub-symbolic computations. The work bridges philosophy, cognitive science, and AI, arguing for pluralism in cognitive architectures and highlighting implications for AI ethics.