# Convergent Representations of Linguistic Constructions in Human and Artificial Neural Systems

arXiv: https://arxiv.org/abs/2603.29617 · HTML: https://mlsanity.com/paper/2603.29617/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [The Platonic Representation Hypothesis](https://mlsanity.com/paper/2405.07987/) (2024)
- [The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative](https://mlsanity.com/paper/2210.13181/) (2022)
- [Word class representations spontaneously emerge in a deep neural network trained on next word prediction](https://mlsanity.com/paper/2302.07588/) (2023)
- [Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions](https://mlsanity.com/paper/2501.04661/) (2025)
- [Analysis of Argument Structure Constructions in a Deep Recurrent Language Model](https://mlsanity.com/paper/2408.03062/) (2024)
- [Analysis of Argument Structure Constructions in the Large Language Model BERT](https://mlsanity.com/paper/2408.04270/) (2024)
- [Cognition spaces: natural, artificial, and hybrid](https://mlsanity.com/paper/2601.12837/) (2026)

## Built on by (most cited first, 1 of 1)

- [Word-Class and Construction-Like Structure Emerges in Neural Successor Representations Trained on Natural Language](https://mlsanity.com/paper/2605.24585/) (2026)

## Abstract (arXiv)

Understanding how the brain processes linguistic constructions is a central challenge in cognitive neuroscience and linguistics. Recent computational studies show that artificial neural language models spontaneously develop differentiated representations of Argument Structure Constructions (ASCs), generating predictions about when and how construction-level information emerges during processing. The present study tests these predictions in human neural activity using electroencephalography (EEG). Ten native English speakers listened to 200 synthetically generated sentences across four construction types (transitive, ditransitive, caused-motion, resultative) while neural responses were recorded. Analyses using time-frequency methods, feature extraction, and machine learning classification revealed construction-specific neural signatures emerging primarily at sentence-final positions, where argument structure becomes fully disambiguated, and most prominently in the alpha band. Pairwise classification showed reliable differentiation, especially between ditransitive and resultative constructions, while other pairs overlapped. Crucially, the temporal emergence and similarity structure of these effects mirror patterns in recurrent and transformer-based language models, where constructional representations arise during integrative processing stages. These findings support the view that linguistic constructions are neurally encoded as distinct form-meaning mappings, in line with Construction Grammar, and suggest convergence between biological and artificial systems on similar representational solutions. More broadly, this convergence is consistent with the idea that learning systems discover stable regions within an underlying representational landscape - recently termed a Platonic representational space - that constrains the emergence of efficient linguistic abstractions.