Neural dynamics must respect language structure for real comprehension
Formal Properties of Language as Constraints on Neural Dynamics
Computation and Language
Summary
Understanding language requires certain basic rules to be followed by brain processes, such as keeping words grouped properly and recognizing repeated patterns. The authors propose a formal framework called Neural Admissibility Program (NAP) that spells out these essential rules as mathematical properties. They test different neural mechanisms and introduce a new one called Meld, which closely matches how language structures work in the brain. Their work shows that just measuring how well a model predicts brain data is not enough to understand if it truly captures language processing.
What this means in practice
- •For computational linguists: Design language processing models that obey key neural constraints to better explain brain-language links.
- •For neural network architects: Develop neural architectures that preserve language-related hierarchical structures via biologically plausible binding operations like Meld.
A theory result. No direct application yet.
Authors
Elliot Murphy
Abstract
What must a neural system be capable of to implement language? Current research annotates stimuli with linguistic variables and tests which electrodes, voxels, or language-model layers predict neural activity. Yet predictive success leaves mechanisms under-constrained. Here, we show that algebraic properties of language specify invariants that mechanisms must preserve: non-associative hierarchical grouping, commutativity, recursive closure, access to substructures, and structured workspace transitions. We term this the Neural Admissibility Program (NAP). Syntactic structure building is analyzed algebraically, with candidate mechanisms offered for each requirement: content-addressable workspace memory, graph-structured transient dynamics scheduling structure-building operations (e.g. stable heteroclinic channels), and a phase-coupled sealing operation recording grouping. Simulations show that a corrected Marcolli-Berwick entropy-optimized binding gate preserves grouping only within a narrow commitment band. As an alternative, we propose a novel neural binding operation we term 'Meld': two constituent populations converge through shared synapses, integrate sublinearly, and saturate. Meld is, to our knowledge, the closest neurally plausible composition law to syntactic Merge. It preserves every NAP invariant, uses known cortical operations, and recovers hierarchical structure at every tested depth and temperature. It predicts that effective population dimensionality separates alternative bracketings and that the composite depends on constituent disagreement. Importantly, the laws decoding bracketing most accurately are a priori inadmissible, showing that decoding accuracy alone cannot adjudicate between mechanisms. By specifying how neural dynamics can remain faithful to linguistic structure, the NAP changes the criterion by which neural implementations of cognition are evaluated.