High local mutual information drives the response in the human language network

Traditionally, syntactic operations are thought of as the core computational machinery that sets human language aside from other animal communication systems. Here, we tested an alternative hypothesis: the primary driver of the response in the language-selective regions of the brain is semantic composition. Using formal machinery from information theory, we estimated the likelihood of semantic composition via mutual information among words in a local linguistic context. Across two fMRI experiments, we examined the processing of veridical sentences as well as syntactically degraded sentences, including sentences where the local context does not support semantic composition. Consistent with behavioral/computational modeling work, syntactic degradedness did not lead to lower responses in the fronto-temporal language-selective network, except for when mutual information among words was low. These results challenge the primacy of syntax in the human language architecture, instead supporting the idea that successful semantic composition is what drives the language network in the brain.