scholarly journals Composition is the core driver of the language-selective network

2018 ◽  
Author(s):  
Francis Mollica ◽  
Evgeniia Diachek ◽  
Zachary Mineroff ◽  
Hope Kean ◽  
Matthew Siegelman ◽  
...  
Keyword(s):  
Language Processing ◽  
Word Order ◽  
Bold Response ◽  
Total N ◽  
The Core ◽  
Sentence Level ◽  
Linguistic Input ◽  
Temporal Language ◽  
Language Areas ◽  
Language Network

AbstractThe fronto-temporal language network responds robustly and selectively to sentences. But the features of linguistic input that drive this response and the computations these language areas support remain debated. Two key features of sentences are typically confounded in natural linguistic input: words in sentences a) are semantically and syntactically combinable into phrase- and clause-level meanings, and b) occur in an order licensed by the language’s grammar. Inspired by recent psycholinguistic work establishing that language processing is robust to word order violations, we hypothesized that the core linguistic computation is composition, and, thus, can take place even when the word order violates the grammatical constraints of the language. This hypothesis predicts that a linguistic string should elicit a sentence-level response in the language network as long as the words in that string can enter into dependency relationships as in typical sentences. We tested this prediction across two fMRI experiments (total N=47) by introducing a varying number of local word swaps into naturalistic sentences, leading to progressively less syntactically well-formed strings. Critically, local dependency relationships were preserved because combinable words remained close to each other. As predicted, word order degradation did not decrease the magnitude of the BOLD response in the language network, except when combinable words were so far apart that composition among nearby words was highly unlikely. This finding demonstrates that composition is robust to word order violations, and that the language regions respond as strongly as they do to naturalistic linguistic input as long as composition can take place.

scholarly journals Composition is the Core Driver of the Language-selective Network

2020 ◽  
Vol 1 (1) ◽  
pp. 104-134 ◽  
Author(s):  
Francis Mollica ◽  
Matthew Siegelman ◽  
Evgeniia Diachek ◽  
Steven T. Piantadosi ◽  
Zachary Mineroff ◽  
...  
Keyword(s):  
Language Processing ◽  
Word Order ◽  
Blood Oxygen Level Dependent ◽  
Blood Oxygen Level ◽  
Total N ◽  
The Core ◽  
Sentence Level ◽  
Linguistic Input ◽  
Language Areas ◽  
Language Network

The frontotemporal language network responds robustly and selectively to sentences. But the features of linguistic input that drive this response and the computations that these language areas support remain debated. Two key features of sentences are typically confounded in natural linguistic input: words in sentences (a) are semantically and syntactically combinable into phrase- and clause-level meanings, and (b) occur in an order licensed by the language’s grammar. Inspired by recent psycholinguistic work establishing that language processing is robust to word order violations, we hypothesized that the core linguistic computation is composition, and, thus, can take place even when the word order violates the grammatical constraints of the language. This hypothesis predicts that a linguistic string should elicit a sentence-level response in the language network provided that the words in that string can enter into dependency relationships as in typical sentences. We tested this prediction across two fMRI experiments (total N = 47) by introducing a varying number of local word swaps into naturalistic sentences, leading to progressively less syntactically well-formed strings. Critically, local dependency relationships were preserved because combinable words remained close to each other. As predicted, word order degradation did not decrease the magnitude of the blood oxygen level–dependent response in the language network, except when combinable words were so far apart that composition among nearby words was highly unlikely. This finding demonstrates that composition is robust to word order violations, and that the language regions respond as strongly as they do to naturalistic linguistic input, providing that composition can take place.

scholarly journals Frontal language areas do not emerge in the absence of temporal language areas: A case study of an individual born without a left temporal lobe

2021 ◽  
Author(s):  
Greta Tuckute ◽  
Alexander Paunov ◽  
Hope Kean ◽  
Hannah Small ◽  
Zachary Mineroff ◽  
...  
Keyword(s):  
Frontal Cortex ◽  
Frontal Lobe ◽  
Temporal Lobe ◽  
Language Processing ◽  
Right Hemisphere ◽  
Left Temporal Lobe ◽  
High Level ◽  
Temporal Language ◽  
Language Areas

High-level language processing is supported by a left-lateralized fronto-temporal brain network. How this network emerges ontogenetically remains debated. Given that frontal cortex in general exhibits protracted development, frontal language areas presumably emerge later and/or mature more slowly than temporal language areas. But are temporal areas necessary for the development of the language areas in the frontal lobe, or do frontal language areas instead emerge independently? We shed light on this question through a case study of an individual (EG) born without a left temporal lobe. We use fMRI methods that have been previously extensively validated for their ability to elicit robust language responses at the individual-subject level. As expected in cases of early left hemisphere (LH) damage, we find that EG has a fully functional language network in her right hemisphere (RH) and performs within the normal range on standardized language assessments. However, her RH frontal language areas have no corresponding LH homotopic areas: no reliable response to language is detected on the lateral surface of EG's left frontal lobe. However, another network implicated in high-level cognition - the domain-general multiple demand, MD, network - is robustly present in both right and left frontal lobes, suggesting that EG's left frontal cortex is capable of supporting non-linguistic cognitive functions. The existence of temporal language areas therefore appears to be a prerequisite for the emergence of the language areas in the frontal lobe.

scholarly journals The universal language network: A cross-linguistic investigation spanning 45 languages and 11 language families.

2021 ◽  
Author(s):  
Dima Ayyash ◽  
Saima Malik-Moraleda ◽  
Jeanne Gallee ◽  
Josef Affourtit ◽  
Malte Hoffman ◽  
...  
Keyword(s):  
Language Processing ◽  
Cognitive Neuroscience ◽  
Large Scale ◽  
Functional Integration ◽  
Brain Regions ◽  
European Languages ◽  
Universal Language ◽  
Neuroscience Research ◽  
Temporal Language ◽  
Language Network

To understand the architecture of human language, it is critical to examine diverse languages; yet most cognitive neuroscience research has focused on a handful of primarily Indo-European languages. Here, we report a large-scale investigation of the fronto-temporal language network across 45 languages and establish the cross-linguistic generality of its key functional properties, including general topography, left-lateralization, strong functional integration among its brain regions, and functional selectivity for language processing. 

scholarly journals P.066 Hemispheric Reorganization of Functional Language Networks Following Neonatal Stroke Supports Language Outcome

2021 ◽  
Vol 48 (s3) ◽  
pp. S38-S38
Author(s):  
Z Emami ◽  
BT Dunkley ◽  
A Robertson ◽  
R Westmacott ◽  
P Krishnan ◽  
...  
Keyword(s):  
Language Processing ◽  
Time Window ◽  
Cross Sectional Study ◽  
Functional Language ◽  
Arterial Ischemic Stroke ◽  
Cross Sectional ◽  
Language Outcome ◽  
Classical Language ◽  
Language Areas ◽  
Language Network

Background: Neonatal Arterial Ischemic Stroke (NAIS) is a common form of paediatric stroke often affecting classical language areas. The post-stroke reorganization of functional language networks may provide insight into later-emerging language deficits and may help to identify at-risk children with NAIS. Methods: A cross-sectional study of fourteen children with left (n=8; 2M; 11.1±2.2 years) or right (n=6; 3M; 12.4±4 years) middle cerebral artery (MCA) NAIS, as well as seven neurotypical children (5M; 13.4±2.7 years), was conducted. Children listened to correct/incorrect syntactic sentences while MEG was recorded, and task-related functional connectivity in the time window and frequency band of interest was determined. Language outcomes were assessed using a battery of neuropsychological tests. Results: A network-based analysis of syntactic language processing (4-7 Hz, 1.2-1.4s) revealed a dysfunctional bilateral frontal-temporal network involving language areas in patients (p=0.01). Patients with right-MCA stroke exhibited a positive correlation between left hemispheric connectivity and measures of language skill (p<0.01), resembling the neurotypical children. In left-MCA stroke patients, greater bilateral connectivity or right laterality in the language network is correlated with good outcome (p<0.05). Conclusions: Depending on the hemispheric location of stroke, certain patterns of language network reorganization may account for impairments in a bilateral frontal-temporal language subnetwork and support language outcome.

scholarly journals Language Prediction Is Reflected by Coupling between Frontal Gamma and Posterior Alpha Oscillations

2018 ◽  
Vol 30 (3) ◽  
pp. 432-447 ◽  
Author(s):  
Lin Wang ◽  
Peter Hagoort ◽  
Ole Jensen
Keyword(s):  
Language Processing ◽  
Visual Word ◽  
Temporal Region ◽  
Alpha Power ◽  
Word Form ◽  
Visual Word Form Area ◽  
Gamma Power ◽  
Temporal Language ◽  
Study Participants ◽  
Language Network

Readers and listeners actively predict upcoming words during language processing. These predictions might serve to support the unification of incoming words into sentence context and thus rely on interactions between areas in the language network. In the current magnetoencephalography study, participants read sentences that varied in contextual constraints so that the predictability of the sentence-final words was either high or low. Before the sentence-final words, we observed stronger alpha power suppression for the highly compared with low constraining sentences in the left inferior frontal cortex, left posterior temporal region, and visual word form area. Importantly, the temporal and visual word form area alpha power correlated negatively with left frontal gamma power for the highly constraining sentences. We suggest that the correlation between alpha power decrease in temporal language areas and left prefrontal gamma power reflects the initiation of an anticipatory unification process in the language network.

Processing subject focus across two Spanish varieties

Probus ◽  
2020 ◽  
Vol 32 (1) ◽  
pp. 93-127
Author(s):  
Bradley Hoot ◽  
Tania Leal
Keyword(s):  
Language Processing ◽  
Sentence Processing ◽  
Word Order ◽  
Judgment Task ◽  
Spanish Speakers ◽  
Experimental Results ◽  
Final Position ◽  
Reading Task ◽  
New Information ◽  
Basic Facts

AbstractLinguists have keenly studied the realization of focus – the part of the sentence introducing new information – because it involves the interaction of different linguistic modules. Syntacticians have argued that Spanish uses word order for information-structural purposes, marking focused constituents via rightmost movement. However, recent studies have challenged this claim. To contribute sentence-processing evidence, we conducted a self-paced reading task and a judgment task with Mexican and Catalonian Spanish speakers. We found that movement to final position can signal focus in Spanish, in contrast to the aforementioned work. We contextualize our results within the literature, identifying three basic facts that theories of Spanish focus and theories of language processing should explain, and advance a fourth: that mismatches in information-structural expectations can induce processing delays. Finally, we propose that some differences in the existing experimental results may stem from methodological differences.

Two New Large Corpora for Vietnamese Aspect-based Sentiment Analysis at Sentence Level

2021 ◽  
Vol 20 (4) ◽  
pp. 1-22
Author(s):  
Dang Van Thin ◽  
Ngan Luu-Thuy Nguyen ◽  
Tri Minh Truong ◽  
Lac Si Le ◽  
Duy Tin Vo
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Network Architectures ◽  
Low Resource ◽  
The Neural Network ◽  
Sentence Level ◽  
Push Forward ◽  
Polarity Classification ◽  
Learning Architectures ◽  
Single Approach

Aspect-based sentiment analysis has been studied in both research and industrial communities over recent years. For the low-resource languages, the standard benchmark corpora play an important role in the development of methods. In this article, we introduce two benchmark corpora with the largest sizes at sentence-level for two tasks: Aspect Category Detection and Aspect Polarity Classification in Vietnamese. Our corpora are annotated with high inter-annotator agreements for the restaurant and hotel domains. The release of our corpora would push forward the low-resource language processing community. In addition, we deploy and compare the effectiveness of supervised learning methods with a single and multi-task approach based on deep learning architectures. Experimental results on our corpora show that the multi-task approach based on BERT architecture outperforms the neural network architectures and the single approach. Our corpora and source code are published on this footnoted site. 1

scholarly journals The Small and Efficient Language Network of Polyglots and Hyper-polyglots

2020 ◽  
Vol 31 (1) ◽  
pp. 62-76
Author(s):  
Olessia Jouravlev ◽  
Zachary Mineroff ◽  
Idan A Blank ◽  
Evelina Fedorenko
Keyword(s):  
Foreign Language ◽  
Language Processing ◽  
Default Mode Network ◽  
Individual Level ◽  
Neural Architecture ◽  
Initial Characterization ◽  
The One ◽  
Multiple Languages ◽  
Language Network

Abstract Acquiring a foreign language is challenging for many adults. Yet certain individuals choose to acquire sometimes dozens of languages and often just for fun. Is there something special about the minds and brains of such polyglots? Using robust individual-level markers of language activity, measured with fMRI, we compared native language processing in polyglots versus matched controls. Polyglots (n = 17, including nine “hyper-polyglots” with proficiency in 10–55 languages) used fewer neural resources to process language: Their activations were smaller in both magnitude and extent. This difference was spatially and functionally selective: The groups were similar in their activation of two other brain networks—the multiple demand network and the default mode network. We hypothesize that the activation reduction in the language network is experientially driven, such that the acquisition and use of multiple languages makes language processing generally more efficient. However, genetic and longitudinal studies will be critical to distinguish this hypothesis from the one whereby polyglots’ brains already differ at birth or early in development. This initial characterization of polyglots’ language network opens the door to future investigations of the cognitive and neural architecture of individuals who gain mastery of multiple languages, including changes in this architecture with linguistic experiences.

scholarly journals Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Siyuan Zhao ◽  
Zhiwei Xu ◽  
Limin Liu ◽  
Mengjie Guo ◽  
Jing Yun
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Word Order ◽  
Text Analysis ◽  
Important Application ◽  
Detection Mechanism ◽  
Short Text

Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains. The deceptive opinion detection is an important application of the existing CNN models. The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions. Online opinions are quite short, varying in their types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.

scholarly journals What is special about L3 processing?

2013 ◽  
Vol 18 (2) ◽  
pp. 130-144 ◽  
Author(s):  
KEES DE BOT ◽  
CAROL JAENSCH
Keyword(s):  
Second Language ◽  
Language Processing ◽  
Language Use ◽  
Fundamental Question ◽  
Third Language ◽  
The Core ◽  
Dynamic Perspective ◽  
Core Issue ◽  
The Brain ◽  
Over Time

While research on third language (L3) and multilingualism has recently shown remarkable growth, the fundamental question of what makes trilingualism special compared to bilingualism, and indeed monolingualism, continues to be evaded. In this contribution we consider whether there is such a thing as a true monolingual, and if there is a difference between dialects, styles, registers and languages. While linguistic and psycholinguistic studies suggest differences in the processing of a third, compared to the first or second language, neurolinguistic research has shown that generally the same areas of the brain are activated during language use in proficient multilinguals. It is concluded that while from traditional linguistic and psycholinguistic perspectives there are grounds to differentiate monolingual, bilingual and multilingual processing, a more dynamic perspective on language processing in which development over time is the core issue, leads to a questioning of the notion of languages as separate entities in the brain.

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