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

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.

Figures (4)

• Figure 1: Conceptual schematic of the AI Private Language thought experiment and the predicted Efficiency Attenuation Phenomenon (EAP). Left: Agents develop an efficient but opaque communication protocol through MARL and cooperation. Right: The same agents are forced to use a pre-defined, human-interpretable symbolic protocol.
• Figure 2: Left: Learning curves for the EC and PSP conditions across training episodes. After around 100 episodes, EC begins to outperform PSP. Right: The EC condition shows steeper learning and converges to higher efficiency (28.7 mean steps) compared to the PSP condition (43.2 mean steps). Shaded regions represent standard error across multiple independent runs. The efficiency gap emerges early and persists throughout training, demonstrating the robustness of the EAP.
• Figure 3: Communication symbol frequency distribution after 300 training episodes. Under the EC condition, symbols A and B form a high-efficiency combination accounting for 77% of usage, demonstrating adaptive optimization. In contrast, the PSP condition maintains a more uniform distribution (approximately 25% each), reflecting the rigidity of the imposed deterministic mapping.
• Figure 4: Evolution of symbol entropy across training episodes. The EC condition shows increasing entropy, stabilizing at 2.6 bits, indicating adaptive information capacity growth. The PSP condition maintains constant low entropy (1.8 bits), reflecting limited expressive flexibility due to its fixed mapping.