The Efficiency Attenuation Phenomenon: A Computational Challenge to the Language of Thought Hypothesis
It challenges the Language of Thought hypothesis, suggesting implications for cognitive science and AI ethics, but is incremental as it builds on existing multi-agent reinforcement learning frameworks.
This paper investigates whether thought requires a language-like format by introducing the Efficiency Attenuation Phenomenon (EAP), where artificial agents using an emergent communication protocol achieve 50.5% higher efficiency than those using a human-like symbolic protocol in a cooperative navigation task.
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.