Autonomous semantics and syntax on-demand in neurocomputational models of language

ROSE is a rare example of a neurocomputational model of language that attempts, and partly manages, to align a formal theory of syntax and parsing with an oscillations-based 'neural code' that could implement the required operations. ROSE successfully reconciles hierarchical and predictive syntactic processing, but I argue that models of language in the brain should make room for the possibility that meaning may also be derived in the absence of any syntactic computation, be it hierarchical or predictive.