Evidence map›Paper›PMID 38948870›Full record

ArticlebioRxiv : the preprint server for biology2024

Linguistic inputs must be syntactically parsable to fully engage the language network.

Carina Kauf, Hee So Kim, Elizabeth J Lee, Niharika Jhingan, Jingyuan Selena She, Maya Taliaferro, Edward Gibson, Evelina Fedorenko

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Carina KaufDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.ORCID 0000-0002-2718-1978
Hee So KimDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
Elizabeth J LeeDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
Niharika JhinganDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
Jingyuan Selena SheDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
Maya TaliaferroDepartment of Psychology, New York University, New York, NY 10012 USA.
Edward GibsonDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
Evelina FedorenkoDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.ORCID 0000-0003-3823-514X

Funding

Computational Neuroscience of Language Processing in the Human BrainU01NS121471 · NINDS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI FEDORENKO, EVELINA, RICHARDSON, ROBERT MARK · 2021 to 2025
$3.2M
Functional reorganization of the language and domain-general multiple demand systems in aphasiaR01DC016950 · NIDCD · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI FEDORENKO, EVELINA, KIRAN, SWATHI · 2019 to 2023
$3.2M
The neural architecture of pragmatic processingR01DC016607 · NIDCD · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI FEDORENKO, EVELINA · 2018 to 2022
$2.4M
NIDCD NIH HHS R01 DC016607NIDCD NIH HHS R01 DC016950NINDS NIH HHS U01 NS121471
6 · The paper itself

Abstract

Human language comprehension is remarkably robust to ill-formed inputs (e.g., word transpositions). This robustness has led some to argue that syntactic parsing is largely an illusion, and that incremental comprehension is more heuristic, shallow, and semantics-based than is often assumed. However, the available data are also consistent with the possibility that humans always perform rule-like symbolic parsing and simply deploy error correction mechanisms to reconstruct ill-formed inputs when needed. We put these hypotheses to a new stringent test by examining brain responses to a) stimuli that should pose a challenge for syntactic reconstruction but allow for complex meanings to be built within local contexts through associative/shallow processing (sentences presented in a backward word order), and b) grammatically well-formed but semantically implausible sentences that should impede semantics-based heuristic processing. Using a novel behavioral syntactic reconstruction paradigm, we demonstrate that backward-presented sentences indeed impede the recovery of grammatical structure during incremental comprehension. Critically, these backward-presented stimuli elicit a relatively low response in the language areas, as measured with fMRI. In contrast, semantically implausible but grammatically well-formed sentences elicit a response in the language areas similar in magnitude to naturalistic (plausible) sentences. In other words, the ability to build syntactic structures during incremental language processing is both necessary and sufficient to fully engage the language network. Taken together, these results provide strongest to date support for a generalized reliance of human language comprehension on syntactic parsing.

Identifiers

PMID38948870
PMCPMC11212959

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.