The Language of Thought hypothesis claims that cognition operates over structured, language-like representations — symbols, compositional syntax, logical form. It's been influential in cognitive science for fifty years.
The authors (arXiv:2603.22312) build a computational test. Two agents learn to cooperate on a navigation task using multi-agent reinforcement learning. In one condition, they develop their own communication protocol. In another, they're forced to use a pre-defined, human-comprehensible symbolic language. If thought requires language-like structure, then agents using explicit symbols should perform at least as well as those using opaque, emergent codes.
They don't. Agents with emergent protocols achieve 50.5% higher efficiency than those using symbolic communication. Forcing language-like structure onto the agents' communication degrades performance — the Efficiency Attenuation Phenomenon.
The result doesn't disprove the Language of Thought. It shows that for at least one class of cognitive tasks — cooperative navigation under partial observability — the optimal internal representation is not symbolic. The emergent protocol is inscrutable to humans but more effective. The agents' “thoughts” are sub-symbolic computations that resist translation into anything resembling language.
The through-claim: structured, interpretable representation has a cost. The symbol grounding that makes thought legible also constrains what can be represented. For some tasks, the most efficient cognition is the kind no one — including the thinking agent — can explain.