The Efficiency Attenuation Phenomenon: A Computational Challenge to the Language of Thought Hypothesis
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914414752432128 |
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| author | Zhang, Di |
| author_facet | Zhang, Di |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_22312 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Efficiency Attenuation Phenomenon: A Computational Challenge to the Language of Thought Hypothesis Zhang, Di Artificial Intelligence Computation and Language Machine Learning 68T05 I.2.6; I.2.11; I.2.0 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. |
| title | The Efficiency Attenuation Phenomenon: A Computational Challenge to the Language of Thought Hypothesis |
| topic | Artificial Intelligence Computation and Language Machine Learning 68T05 I.2.6; I.2.11; I.2.0 |
| url | https://arxiv.org/abs/2603.22312 |