Elliot Murphy

New paper published in Cognitive Neuroscience on the neural code for natural language syntax (PDF here). This paper explores what neural mechanisms can potentially satisfy the demands of the free non-associative commutative magma emerging from contemporary formulations of a Merge-based syntax. These are proposed to be a delimited set of neural signatures that are hypothesized to constitute a human-specific “neural grammar” that gives rise to our capacity for language.

“Processing natural language syntax requires a negotiation between symbolic and subsymbolic representations. Building on a recent neurocomputational architecture for syntax that scales from single units to inter-areal dynamics (ROSE), I discuss the prospects of reconciling the neural code for hierarchical syntax with predictive processes. Here, the higher levels of ROSE provide instructions for symbolic phrase structure representations (S/E), while the lower levels provide probabilistic aspects of linguistic processing (R/O), with different types of cross-frequency coupling being hypothesized to interface these domains. I argue that ROSE provides a possible infrastructure for flexibly implementing distinct types of minimalist grammar parsers for the real-time processing of language. This perspective helps to furnish a more restrictive ‘core language network’ in the brain than approaches that isolate general sentence composition. I define the language network as being critically involved in executing specific parsing operations (i.e., establishing phrasal categories, tree-structure depth, resolving dependencies, and retrieving proprietary lexico-syntactic representations), capturing these network-defining operations jointly with probabilistic aspects of parsing. ROSE offers a ‘mesoscopic protectorate’ for natural language; an intermediate level of emergent organizational complexity that demands multi-scale modeling. By drawing principled relations across computational, algorithmic and implementational Marrian levels, ROSE offers new constraints on what a unified neurocomputational settlement for natural language syntax might look like, providing a tentative scaffold for a ‘Universal Neural Grammar’ – a species-specific format for neurally organizing the construction of compositional syntactic structures, which matures in accordance with a genetically determined biological matrix.”