# Neurocomputational Mechanisms of Syntactic Transfer in Bilingual Sentence Production

arXiv: https://arxiv.org/abs/2601.18056 · HTML: https://mlsanity.com/paper/2601.18056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 82 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters](https://mlsanity.com/paper/1410.6031/) (2014)
- [ROSE: A Neurocomputational Architecture for Syntax](https://mlsanity.com/paper/2303.08877/) (2023)
- [Natural Language Syntax Complies with the Free-Energy Principle](https://mlsanity.com/paper/2210.15098/) (2022)
- [A Structure-based Memory Maintenance Model for Neural Tracking of Linguistic Structures](https://mlsanity.com/paper/2002.11870/) (2020)

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

- [The Impact of Editorial Intervention on Detecting Native Language Traces](https://mlsanity.com/paper/2605.10216/) (2026)

## Abstract (arXiv)

We discuss the benefits of incorporating oscillatory neural mechanisms into the study of bilingual production errors and their traditionally documented timing signatures (e.g., event-related potentials), which can offer new implementational-level constraints for theories of bilingualism. We argue that a recent neural model of language, ROSE, can offer a neurocomputational account of syntactic transfer in bilingual production, capturing some of its formal properties and the scope of morphosyntactic sequencing failure modes. We take as a case study cross-linguistic influence (CLI) and attendant theories of functional inhibition/competition, and present these as being driven by specific oscillatory failure modes during L2 sentence planning. We argue that modeling CLI in this way not only offers the kinds of linking hypotheses ROSE was built to encourage, but also licenses the exploration of more spatiotemporally complex biomarkers of non-native processing than more commonly discussed neural signatures.