Статті в журналах: "Brain language processing"

1

Bick, Atira S., Gadi Goelman, and Ram Frost. "Hebrew Brain vs. English Brain: Language Modulates the Way It Is Processed." Journal of Cognitive Neuroscience 23, no. 9 (2011): 2280–90. http://dx.doi.org/10.1162/jocn.2010.21583.

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Is language processing universal? How do the specific properties of each language influence the way it is processed? In this study, we compare the neural correlates of morphological processing in Hebrew—a Semitic language with a rich and systematic morphology, to those revealed in English—an Indo-European language with a linear morphology. Using fMRI, we show that while in the bilingual brain both languages involve a common neural circuitry in processing morphological structure, this activation is significantly modulated by the different aspects of language. Whereas in Hebrew, morphological processing is independent of semantics, in English, morphological activation is clearly modulated by semantic overlap. These findings suggest that the processes involved in reading words are not universal, and therefore impose important constraints on current models of visual word recognition.

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2

Buchweitz, Augusto. "Brain and Language: an overview of neuroimaging studies of bilingual language processing." Revista Brasileira de Linguística Aplicada 5, no. 2 (2005): 87–99. http://dx.doi.org/10.1590/s1984-63982005000200004.

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Six articles combining the study of bilinguals and neuroimaging techniques are discussed. The objective is to seek for contributions from neuroimaging studies for the understanding of what goes on in the bilingual brain that processes two languages, and of what goes on, comparatively, in terms of brain activation of each language. Studies show that highly proficient bilinguals activate the same areas in the brain for both the first and second languages. This indicates that the second language becomes part of the speaker's procedural knowledge.

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3

Malaia, Evie, and Ronnie B. Wilbur. "Early acquisition of sign language." Sign Language and Linguistics 13, no. 2 (2010): 183–99. http://dx.doi.org/10.1075/sll.13.2.03mal.

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Early acquisition of a natural language, signed or spoken, has been shown to fundamentally impact both one’s ability to use the first language, and the ability to learn subsequent languages later in life (Mayberry 2007, 2009). This review summarizes a number of recent neuroimaging studies in order to detail the neural bases of sign language acquisition. The logic of this review is to present research reports that contribute to the bigger picture showing that people who acquire a natural language, spoken or signed, in the normal way possess specialized linguistic abilities and brain functions that are missing or deficient in people whose exposure to natural language is delayed or absent. Comparing the function of each brain region with regards to the processing of spoken and sign languages, we attempt to clarify the role each region plays in language processing in general, and to outline the challenges and remaining questions in understanding language processing in the brain.

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4

Mengotti, Paola, Corrado Corradi-Dell’Acqua, Gioia A. L. Negri, Maja Ukmar, Valentina Pesavento, and Raffaella I. Rumiati. "Selective imitation impairments differentially interact with language processing." Brain 136, no. 8 (2013): 2602–18. http://dx.doi.org/10.1093/brain/awt194.

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5

DE BOT, KEES, and CAROL JAENSCH. "What is special about L3 processing?" Bilingualism: Language and Cognition 18, no. 2 (2013): 130–44. http://dx.doi.org/10.1017/s1366728913000448.

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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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6

Ibrayeva, Zh. "THE ROLE OF NEUROLINGUISTIC RESEARCH IN THE STUDY OF BILINGUALISM." BULLETIN Series of Philological Sciences 75, no. 1 (2021): 66–71. http://dx.doi.org/10.51889/2021-1.1728-7804.11.

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The use of two or more languages is common in most countries of the world. However, until recently, bilingualism was considered as a factor that complicates the processing of speech, cognition and the brain. In the past 25 years there have been a surge in research on bilingualism, including the study, mastery and processing of languages, their cognitive and neural foundations, and the lifelong implications of bilingualism for cognition and the brain. Contrary to the belief that bilingualism complicates the language system, new research demonstrates that all known and used languages become part of the same language system. The interactions that occur when using the two languages have consequences for mind and the brain and indeed for language processing itself but these implications are not additive. Thus, bilingualism helps to uncover the fundamental architecture and language processing mechanisms that locates differently in monolingual speakers.

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7

Christiansen, Morten H., and Nick Chater. "Language as shaped by the brain." Behavioral and Brain Sciences 31, no. 5 (2008): 489–509. http://dx.doi.org/10.1017/s0140525x08004998.

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AbstractIt is widely assumed that human learning and the structure of human languages are intimately related. This relationship is frequently suggested to derive from a language-specific biological endowment, which encodes universal, but communicatively arbitrary, principles of language structure (a Universal Grammar or UG). How might such a UG have evolved? We argue that UG could not have arisen either by biological adaptation or non-adaptationist genetic processes, resulting in alogical problem of language evolution. Specifically, as the processes of language change are much more rapid than processes of genetic change, language constitutes a “moving target” both over time and across different human populations, and, hence, cannot provide a stable environment to which language genes could have adapted. We conclude that a biologically determined UG is not evolutionarily viable. Instead, the original motivation for UG – the mesh between learners and languages – arises because language has been shaped to fit the human brain, rather than vice versa. Following Darwin, we view language itself as a complex and interdependent “organism,” which evolves under selectional pressures from human learning and processing mechanisms. That is, languages themselves are shaped by severe selectional pressure from each generation of language users and learners. This suggests that apparently arbitrary aspects of linguistic structure may result from general learning and processing biases deriving from the structure of thought processes, perceptuo-motor factors, cognitive limitations, and pragmatics.

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8

Proverbio, Alice Mado, Barbara Čok, and Alberto Zani. "Electrophysiological Measures of Language Processing in Bilinguals." Journal of Cognitive Neuroscience 14, no. 7 (2002): 994–1017. http://dx.doi.org/10.1162/089892902320474463.

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The aim of the present study was to investigate how multiple languages are represented in the human brain. Event-related brain potentials (ERPs) were recorded from right-handed polyglots and monolinguals during a task involving silent reading. The participants in the experiment were nine Italian monolinguals and nine Italian/Slovenian bilinguals of a Slovenian minority in Trieste; the bilinguals, highly fluent in both languages, had spoken both languages since birth. The stimuli were terminal words that would correctly complete a short, meaningful, previously shown sentence, or else were semantically or syntactically incorrect. The task consisted in deciding whether the sentences were well formed or not, giving the response by pressing a button. Both groups read the same set of 200 Italian sentences to compare the linguistic processing, while the bilinguals also received a set of 200 Slovenian sentences, comparable in complexity and length, to compare the processing of the two languages within the group. For the bilinguals, the ERP results revealed a strong, left-sided activation, reflected by the N1 component, of the occipito-temporal regions dedicated to orthographic processing, with a latency of about 150 msec for Slovenian words, but bilateral activation of the same areas for Italian words, which was also displayed by topographical mapping. In monolinguals, semantic error produced a long-lasting negative response (N2 and N4) that was greater over the right hemisphere, whereas syntactic error activated mostly the left hemisphere. Conversely, in the bilinguals, semantic incongruence resulted in greater response over the left hemisphere than over the right. In this group, the P615 syntactical error responses were of equal amplitude on both hemispheres for Italian words and greater on the right side for Slovenian words. The present findings support the view that there are inter- and intrahemispheric brain activation asymmetries when monolingual and bilingual speakers comprehend written language. The fact that the bilingual speakers in the present study were highly fluent and had acquired both languages in early infancy suggests that the brain activation patterns do not depend on the age of acquisition or the fluency level, as in the case of late, not-so-proficient L2 language learners, but on the functional organization of the bilinguals' brain due to polyglotism and based on brain plasticity.

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9

Harasty, J., J. R. Binder, J. A. Frost, et al. "Language processing in both sexes: evidence from brain studies." Brain 123, no. 2 (2000): 404–6. http://dx.doi.org/10.1093/brain/123.2.404.

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10

Ge, Jianqiao, Gang Peng, Bingjiang Lyu, et al. "Cross-language differences in the brain network subserving intelligible speech." Proceedings of the National Academy of Sciences 112, no. 10 (2015): 2972–77. http://dx.doi.org/10.1073/pnas.1416000112.

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How is language processed in the brain by native speakers of different languages? Is there one brain system for all languages or are different languages subserved by different brain systems? The first view emphasizes commonality, whereas the second emphasizes specificity. We investigated the cortical dynamics involved in processing two very diverse languages: a tonal language (Chinese) and a nontonal language (English). We used functional MRI and dynamic causal modeling analysis to compute and compare brain network models exhaustively with all possible connections among nodes of language regions in temporal and frontal cortex and found that the information flow from the posterior to anterior portions of the temporal cortex was commonly shared by Chinese and English speakers during speech comprehension, whereas the inferior frontal gyrus received neural signals from the left posterior portion of the temporal cortex in English speakers and from the bilateral anterior portion of the temporal cortex in Chinese speakers. Our results revealed that, although speech processing is largely carried out in the common left hemisphere classical language areas (Broca’s and Wernicke’s areas) and anterior temporal cortex, speech comprehension across different language groups depends on how these brain regions interact with each other. Moreover, the right anterior temporal cortex, which is crucial for tone processing, is equally important as its left homolog, the left anterior temporal cortex, in modulating the cortical dynamics in tone language comprehension. The current study pinpoints the importance of the bilateral anterior temporal cortex in language comprehension that is downplayed or even ignored by popular contemporary models of speech comprehension.

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11

van den Noort, Maurits, Esli Struys, Kayoung Kim, et al. "Multilingual processing in the brain." International Journal of Multilingualism 11, no. 2 (2013): 182–201. http://dx.doi.org/10.1080/14790718.2013.791298.

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12

Novaes, Celso V. "Language and the Brain: Representation and Processing (review)." Language 77, no. 4 (2001): 867–68. http://dx.doi.org/10.1353/lan.2001.0232.

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13

Mayberry, Rachel I., Tristan Davenport, Austin Roth, and Eric Halgren. "Neurolinguistic processing when the brain matures without language." Cortex 99 (February 2018): 390–403. http://dx.doi.org/10.1016/j.cortex.2017.12.011.

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14

Flöel, Agnes. "Non-invasive brain stimulation and language processing in the healthy brain." Aphasiology 26, no. 9 (2012): 1082–102. http://dx.doi.org/10.1080/02687038.2011.589892.

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15

Johansson, Christer, and Per Olav Folgerø. "Is Reduced Visual Processing the Price of Language?" Brain Sciences 12, no. 6 (2022): 771. http://dx.doi.org/10.3390/brainsci12060771.

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We suggest a later timeline for full language capabilities in Homo sapiens, placing the emergence of language over 200,000 years after the emergence of our species. The late Paleolithic period saw several significant changes. Homo sapiens became more gracile and gradually lost significant brain volumes. Detailed realistic cave paintings disappeared completely, and iconic/symbolic ones appeared at other sites. This may indicate a shift in perceptual abilities, away from an accurate perception of the present. Language in modern humans interact with vision. One example is the McGurk effect. Studies show that artistic abilities may improve when language-related brain areas are damaged or temporarily knocked out. Language relies on many pre-existing non-linguistic functions. We suggest that an overwhelming flow of perceptual information, vision, in particular, was an obstacle to language, as is sometimes implied in autism with relative language impairment. We systematically review the recent research literature investigating the relationship between language and perception. We see homologues of language-relevant brain functions predating language. Recent findings show brain lateralization for communicative gestures in other primates without language, supporting the idea that a language-ready brain may be overwhelmed by raw perception, thus blocking overt language from evolving. We find support in converging evidence for a change in neural organization away from raw perception, thus pushing the emergence of language closer in time. A recent origin of language makes it possible to investigate the genetic origins of language.

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16

Cardin, Velia, Eleni Orfanidou, Lena Kästner, et al. "Monitoring Different Phonological Parameters of Sign Language Engages the Same Cortical Language Network but Distinctive Perceptual Ones." Journal of Cognitive Neuroscience 28, no. 1 (2016): 20–40. http://dx.doi.org/10.1162/jocn_a_00872.

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The study of signed languages allows the dissociation of sensorimotor and cognitive neural components of the language signal. Here we investigated the neurocognitive processes underlying the monitoring of two phonological parameters of sign languages: handshape and location. Our goal was to determine if brain regions processing sensorimotor characteristics of different phonological parameters of sign languages were also involved in phonological processing, with their activity being modulated by the linguistic content of manual actions. We conducted an fMRI experiment using manual actions varying in phonological structure and semantics: (1) signs of a familiar sign language (British Sign Language), (2) signs of an unfamiliar sign language (Swedish Sign Language), and (3) invented nonsigns that violate the phonological rules of British Sign Language and Swedish Sign Language or consist of nonoccurring combinations of phonological parameters. Three groups of participants were tested: deaf native signers, deaf nonsigners, and hearing nonsigners. Results show that the linguistic processing of different phonological parameters of sign language is independent of the sensorimotor characteristics of the language signal. Handshape and location were processed by different perceptual and task-related brain networks but recruited the same language areas. The semantic content of the stimuli did not influence this process, but phonological structure did, with nonsigns being associated with longer RTs and stronger activations in an action observation network in all participants and in the supramarginal gyrus exclusively in deaf signers. These results suggest higher processing demands for stimuli that contravene the phonological rules of a signed language, independently of previous knowledge of signed languages. We suggest that the phonological characteristics of a language may arise as a consequence of more efficient neural processing for its perception and production.

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17

Peña, Marcela, and Lucia Melloni. "Brain Oscillations during Spoken Sentence Processing." Journal of Cognitive Neuroscience 24, no. 5 (2012): 1149–64. http://dx.doi.org/10.1162/jocn_a_00144.

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Spoken sentence comprehension relies on rapid and effortless temporal integration of speech units displayed at different rates. Temporal integration refers to how chunks of information perceived at different time scales are linked together by the listener in mapping speech sounds onto meaning. The neural implementation of this integration remains unclear. This study explores the role of short and long windows of integration in accessing meaning from long samples of speech. In a cross-linguistic study, we explore the time course of oscillatory brain activity between 1 and 100 Hz, recorded using EEG, during the processing of native and foreign languages. We compare oscillatory responses in a group of Italian and Spanish native speakers while they attentively listen to Italian, Japanese, and Spanish utterances, played either forward or backward. The results show that both groups of participants display a significant increase in gamma band power (55–75 Hz) only when they listen to their native language played forward. The increase in gamma power starts around 1000 msec after the onset of the utterance and decreases by its end, resembling the time course of access to meaning during speech perception. In contrast, changes in low-frequency power show similar patterns for both native and foreign languages. We propose that gamma band power reflects a temporal binding phenomenon concerning the coordination of neural assemblies involved in accessing meaning of long samples of speech.

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18

Azaiez, Najla, Otto Loberg, Kaisa Lohvansuu, Sari Ylinen, Jarmo A. Hämäläinen, and Paavo H. T. Leppänen. "Discriminatory Brain Processes of Native and Foreign Language in Children with and without Reading Difficulties." Brain Sciences 13, no. 1 (2022): 76. http://dx.doi.org/10.3390/brainsci13010076.

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The association between impaired speech perception and reading difficulty has been well established in native language processing, as can be observed from brain activity. However, there has been scarce investigation of whether this association extends to brain activity during foreign language processing. The relationship between reading skills and neuronal speech representation of foreign language remains unclear. In the present study, we used event-related potentials (ERPs) with high-density EEG to investigate this question. Eleven- to 13-year-old children typically developed (CTR) or with reading difficulties (RD) were tested via a passive auditory oddball paradigm containing native (Finnish) and foreign (English) speech items. The change-detection-related ERP responses, the mismatch response (MMR), and the late discriminative negativity (LDN) were studied. The cluster-based permutation tests within and between groups were performed. The results showed an apparent language effect. In the CTR group, we found an atypical MMR in the foreign language processing and a larger LDN response for speech items containing a diphthong in both languages. In the RD group, we found unstable MMR with lower amplitude and a nonsignificant LDN response. A deficit in the LDN response in both languages was found within the RD group analysis. Moreover, we observed larger brain responses in the RD group and a hemispheric polarity reversal compared to the CTR group responses. Our results provide new evidence that language processing differed between the CTR and RD groups in early and late discriminatory responses and that language processing is linked to reading skills in both native and foreign language contexts.

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19

Sharma, Gourav. "Automated Brain Tumor Prediction System using Natural Language Processing (NLP)." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3784–87. http://dx.doi.org/10.22214/ijraset.2021.37196.

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In this paper, we proposed an Automated Brain Tumor Prediction System which predicts Brain Tumor through symptoms in several diseases using Natural Language Processing (NLP). Term Frequency Inverse Document Frequency (TF-IDF) is used for calculating term weighting of terms on different disease’s symptoms. Cosine Similarity Measure and Euclidean Distance are used for calculating angular and linear distance respectively between diseases and symptoms for getting ranking of the Brain Tumor in the ranked diseases. A novel mathematical strategy is used here for predicting chance of Brain Tumor through symptoms in several diseases. According to the proposed novel mathematical strategy, the chance of the Brain Tumor is proportional to the obtained similarity value of the Brain Tumor when symptoms are queried and inversely proportional to the rank of the Brain Tumor in several diseases and the maximum similarity value of the Brain Tumor, where all symptoms of Brain Tumor are present.

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20

Helenius, P., T. Parviainen, R. Paetau, and R. Salmelin. "Neural processing of spoken words in specific language impairment and dyslexia." Brain 132, no. 7 (2009): 1918–27. http://dx.doi.org/10.1093/brain/awp134.

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21

Watkins, Kate E., Alan Cowey, Iona Alexander, et al. "Language networks in anophthalmia: maintained hierarchy of processing in ‘visual’ cortex." Brain 135, no. 5 (2012): 1566–77. http://dx.doi.org/10.1093/brain/aws067.

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22

Saygin, A. P. "Neural resources for processing language and environmental sounds: Evidence from aphasia." Brain 126, no. 4 (2003): 928–45. http://dx.doi.org/10.1093/brain/awg082.

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23

Kailash Nath Tripathi, Et al. "Functional Brain Connectivity Differences between Aphasic and Neurotypical Brains." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2024): 4714–18. http://dx.doi.org/10.17762/ijritcc.v11i9.10022.

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Aphasia is a language disorder that can arise from brain damage, leading to difficulties in understanding, generating, or using language. Although the precise neural mechanisms are not fully elucidated, it is hypothesized that these disruptions involve altered communication and interaction among brain regions. In this study, functional magnetic resonance imaging (fMRI) was employed to assess functional connectivity in both individuals with aphasia and neurotypical individuals. Functional connectivity is a measure of the way that brain regions communicate and interact with each other. The study participants performed a series of language-processing tasks, while their fMRI data was collected. The study's findings showed that individuals with aphasia had unique functional brain connectivity patterns when compared to neurotypical individuals. These distinctions were most prominent in the left hemisphere, which is conventionally associated with language processing. In particular, individuals with aphasia demonstrated diminished functional connectivity between the language regions in the left hemisphere and other brain regions, including those in the right hemisphere and the frontal lobe. The study's findings suggest that differences in functional brain connectivity may contribute to language deficits in aphasia. The study's findings also hold significant implications for advancing our understanding of the neurological underpinnings of aphasia and the potential for improved diagnostic and therapeutic methods for individuals with this condition.

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24

Hale, John T., Luca Campanelli, Jixing Li, Shohini Bhattasali, Christophe Pallier, and Jonathan R. Brennan. "Neurocomputational Models of Language Processing." Annual Review of Linguistics 8, no. 1 (2022): 427–46. http://dx.doi.org/10.1146/annurev-linguistics-051421-020803.

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Efforts to understand the brain bases of language face the Mapping Problem: At what level do linguistic computations and representations connect to human neurobiology? We review one approach to this problem that relies on rigorously defined computational models to specify the links between linguistic features and neural signals. Such tools can be used to estimate linguistic predictions, model linguistic features, and specify a sequence of processing steps that may be quantitatively fit to neural signals collected while participants use language. Progress has been helped by advances in machine learning, attention to linguistically interpretable models, and openly shared data sets that allow researchers to compare and contrast a variety of models. We describe one such data set in detail in the Supplemental Appendix .

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25

Sholihah, Rizki Amalia. "Language and Brain: Neurological Aspects in Language Acquisition." MUHARRIK: Jurnal Dakwah dan Sosial 5, no. 1 (2022): 220–30. http://dx.doi.org/10.37680/muharrik.v5i1.1069.

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The human nervous system consists of two parts, namely the spine and the brain. The brain itself consists of the brain stem and cerebral cortex, consisting of the right and left hemispheres. Each hemisphere is divided into four lobes: the frontal lobe, occipital lobe, temporal lobe, and parietal lobe, where each lobe has its function—the brain experiences both growth regarding its parts and mass or weight. The left and right hemispheres have their respective roles or functions, leading to a tendency to use hemispheres. The left hemisphere is more dominantly responsible for language, logical and analytical matters. At the same time, the right hemisphere is more intuitive and imaginative. Concerning producing speech, the brain has a part called the Broca region. Then there is the Wernicke area which is responsible for speech comprehension. These two regions will work together for language processing in the brain. Besides, there is a system related to the workings of emotions in humans called the limbic system in the brain.

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26

BOSCH, SINA, and ALINA LEMINEN. "ERP priming studies of bilingual language processing." Bilingualism: Language and Cognition 21, no. 3 (2018): 462–70. http://dx.doi.org/10.1017/s1366728917000700.

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The aim of this review is to provide a selective overview of priming studies which have employed the event-related brain potential (ERP) technique in order to investigate bilingual language processing. The priming technique can reveal an implicit memory effect in which exposure to one stimulus influences the processing of another stimulus. Behavioral approaches, such as measuring reaction times, may not always be enough for providing a full view on the exact mechanisms and the time-course of language comprehension. Instead, ERPs have a time-resolution of a millisecond and hence they offer a precise temporal overview of the underlying neural processes involved in language processing. In our review, we summarize experimental research that has combined priming with ERP measurements, thus creating a valuable tool for examining the neurophysiological correlates of language processing in the bilingual brain.

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27

Jouravlev, Olessia, Zachary Mineroff, Idan A. Blank, and Evelina Fedorenko. "The Small and Efficient Language Network of Polyglots and Hyper-polyglots." Cerebral Cortex 31, no. 1 (2020): 62–76. http://dx.doi.org/10.1093/cercor/bhaa205.

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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.

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28

CODERRE, EMILY L., JASON F. SMITH, WALTER J. B. VAN HEUVEN, and BARRY HORWITZ. "The functional overlap of executive control and language processing in bilinguals." Bilingualism: Language and Cognition 19, no. 3 (2015): 471–88. http://dx.doi.org/10.1017/s1366728915000188.

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The need to control multiple languages is thought to require domain-general executive control in bilinguals such that the executive control and language systems become interdependent. However, there has been no systematic investigation into how and where executive control and language processes overlap in the bilingual brain. If the concurrent recruitment of executive control during bilingual language processing is domain-general and extends to non-linguistic control, we hypothesize that regions commonly involved in language processing, linguistic control, and non-linguistic control may be selectively altered in bilinguals compared to monolinguals. A conjunction of functional magnetic resonance imaging (fMRI) data from a flanker task with linguistic and non-linguistic distractors and a semantic categorization task showed functional overlap in the left inferior frontal gyrus (LIFG) in bilinguals, whereas no overlap occurred in monolinguals. This research therefore identifies a neural locus of functional overlap of language and executive control in the bilingual brain.

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29

Voets, N. L., J. E. Adcock, D. E. Flitney, et al. "Distinct right frontal lobe activation in language processing following left hemisphere injury." Brain 129, no. 3 (2005): 754–66. http://dx.doi.org/10.1093/brain/awh679.

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30

BEIM GRABEN, PETER, BRYAN JURISH, DOUGLAS SADDY, and STEFAN FRISCH. "LANGUAGE PROCESSING BY DYNAMICAL SYSTEMS." International Journal of Bifurcation and Chaos 14, no. 02 (2004): 599–621. http://dx.doi.org/10.1142/s0218127404009326.

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We describe a part of the stimulus sentences of a German language processing ERP experiment using a context-free grammar and represent different processing preferences by its unambiguous partitions. The processing is modeled by deterministic pushdown automata. Using a theorem proven by Moore, we map these automata onto discrete time dynamical systems acting at the unit square, where the processing preferences are represented by a control parameter. The actual states of the automata are rectangles lying in the unit square that can be interpreted as cylinder sets in the context of symbolic dynamics theory. We show that applying a wrong processing preference to a certain input string leads to an unwanted invariant set in the parsers dynamics. Then, syntactic reanalysis and repair can be modeled by a switching of the control parameter — in analogy to phase transitions observed in brain dynamics. We argue that ERP components are indicators of these bifurcations and propose an ERP-like measure of the parsing model.

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31

Chee, M. W. L., and C. S. Soon. "Seeing How We Think About Words Using BOLD Contrast fMR Imaging." Annals of the Academy of Medicine, Singapore 32, no. 4 (2003): 490–94. http://dx.doi.org/10.47102/annals-acadmedsg.v32n4p490.

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This review examines how blood oxygen level-dependent functional magnetic resonance imaging (fMRI) may be harnessed to study the brain when it engages in language processing tasks. This method makes clinical and scientific contributions to understanding language function. Issues such as the lateralisation of language function, brain plasticity in health, ageing and neurological disease, and as well as how 2 different languages are processed, may all be evaluated by fMRI.

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Konyakhina, Liudmila, and Andrey Ivanov. "Musical Competence and Second Language Learning." Nizhny Novgorod Linguistics University Bulletin, no. 54 (June 30, 2021): 149–64. http://dx.doi.org/10.47388/2072-3490/lunn2021-54-2-149-164.

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In recent years, we have witnessed a renewal of interest in the language — music relationship due to the development of cognitive science and the advent of brain imaging methods, such as positron emission tomography, functional magnetic resonance imaging, magnetoencephalography, electroencephalography, and event-related brain potentials, which has led to a number of major discoveries. The relationship between music and language has been examined from many different perspectives. Taken together, these findings indicate that musical competence positively influences some aspects of speech processing, from auditory perception to speech production and may benefit second language acquisition. In this review, we focus on the main results of the current research, discuss several interpretations that may account for the influence of musical competence on speech processing in native and foreign languages, and propose new directions for future research.

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Black, Peter McL, Sandra E. Black, and Janet A. Droge. "Three Models of Human Language." Neurosurgery 19, no. 2 (1986): 308–15. http://dx.doi.org/10.1227/00006123-198608000-00027.

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Abstract Three models of human language processing can be discerned in contemporary neurobiology: the Wernicke-Broca model, primarily derived from studies of stroke and other brain lesions; the model developed by Dr. George Ojemann from electrical stimulation mapping of the cerebral cortex; and the linguistic model, evolved from Noam Chomsky's linguistic theories. The Wernicke-Broca model employs a posterior-sensory anterior-motor conception of brain language processing that is substantially different from the more modular conception developed from electrical stimulation mapping. The linguistic model attempts to explain language in terms of hierarchical mental function.

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34

Prystauka, Yanina, Vincent DeLuca, Alicia Luque, Toms Voits, and Jason Rothman. "Cognitive Neuroscience Perspectives on Language Acquisition and Processing." Brain Sciences 13, no. 12 (2023): 1613. http://dx.doi.org/10.3390/brainsci13121613.

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FANG, Xiao-Ping, and You-Yi LIU. "The Brain Basis of Syntactic Processing During Language Comprehension." Advances in Psychological Science 20, no. 12 (2013): 1940–51. http://dx.doi.org/10.3724/sp.j.1042.2012.01940.

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36

Koelsch, Stefan, Elisabeth Kasper, Daniela Sammler, Katrin Schulze, Thomas Gunter, and Angela D. Friederici. "Music, language and meaning: brain signatures of semantic processing." Nature Neuroscience 7, no. 3 (2004): 302–7. http://dx.doi.org/10.1038/nn1197.

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37

Grimshaw, Gina M., Amy Ramos, Colby Carter, Julie-Anne Séguin, and Hazel K. Godfrey. "Effects of emotion on brain organization for language processing." Brain and Cognition 67 (June 2008): 9. http://dx.doi.org/10.1016/j.bandc.2008.02.114.

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38

Goldstein, Ariel, Zaid Zada, Eliav Buchnik, et al. "Shared computational principles for language processing in humans and deep language models." Nature Neuroscience 25, no. 3 (2022): 369–80. http://dx.doi.org/10.1038/s41593-022-01026-4.

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AbstractDeparting from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.

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Conklin, Kathy, and Norbert Schmitt. "The Processing of Formulaic Language." Annual Review of Applied Linguistics 32 (March 2012): 45–61. http://dx.doi.org/10.1017/s0267190512000074.

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It is generally accepted that we store representations of individual words in our mental lexicon. There is growing agreement that the lexicon also contains formulaic language (How are you? kick the bucket). In fact, there are compelling reasons to think that the brain represents formulaic sequences in long-term memory, bypassing the need to compose them online through word selection and grammatical sequencing in capacity-limited working memory. The research surveyed in this chapter strongly supports the position that there is an advantage in the way that native speakers process formulaic language compared to nonformulaic language. This advantage extends to the access and use of different types of formulaic language, including idioms, binomials, collocations, and lexical bundles. However, the evidence is mixed for nonnative speakers. While very proficient nonnatives sometimes exhibit processing advantages similar to natives, less proficient learners often have been shown to process formulaic language in a word-by-word manner similar to nonformulaic language. Furthermore, if the formulaic language is idiomatic (where the meaning cannot be understood from the component words), the figurative meanings can be much more difficult to process for nonnatives than nonidiomatic, nonformulaic language.

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40

Corina, David P., Lucila San Jose-Robertson, Andre Guillemin, Julia High, and Allen R. Braun. "Language Lateralization in a Bimanual Language." Journal of Cognitive Neuroscience 15, no. 5 (2003): 718–30. http://dx.doi.org/10.1162/jocn.2003.15.5.718.

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Unlike spoken languages, sign languages of the deaf make use of two primary articulators, the right and left hands, to produce signs. This situation has no obvious parallel in spoken languages, in which speech articulation is carried out by symmetrical unitary midline vocal structures. This arrangement affords a unique opportunity to examine the robustness of linguistic systems that underlie language production in the face of contrasting articulatory demands and to chart the differential effects of handedness for highly skilled movements. Positron emission tomography (PET) technique was used to examine brain activation in 16 deaf users of American Sign Language (ASL) while subjects generated verb signs independently with their right dominant and left nondominant hands (compared to the repetition of noun signs). Nearly identical patterns of left inferior frontal and right cerebellum activity were observed. This pattern of activation during signing is consistent with patterns that have been reported for spoken languages including evidence for specializations of inferior frontal regions related to lexical–semantic processing, search and retrieval, and phonological encoding. These results indicate that lexical–semantic processing in production relies upon left-hemisphere regions regardless of the modality in which a language is realized, and that this left-hemisphere activation is stable, even in the face of conflicting articulatory demands. In addition, these data provide evidence for the role of the right posterolateral cerebellum in linguistic–cognitive processing and evidence of a left ventral fusiform contribution to sign language processing

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41

Frost, J. A. "Language processing is strongly left lateralized in both sexes: Evidence from functional MRI." Brain 122, no. 2 (1999): 199–208. http://dx.doi.org/10.1093/brain/122.2.199.

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42

Swann, Philip. "Greenfield on language, tools, and brain." Behavioral and Brain Sciences 21, no. 1 (1998): 155–59. http://dx.doi.org/10.1017/s0140525x98220966.

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Greenfield (1991t) fails in an attempt to defend her own original synthesis of cognitivist and nativist accounts of language development. The proposed synchronous stages of object and phoneme combination are not supported by the empirical data she presents. The functional specification of hypothetical neural circuits is almost entirely speculative. Nor is it likely that new data could save her model, since it is formulated in a simplistic information processing framework that is now of little more than historical interest.

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43

Pechenkova, E. V., Ya R. Panikratova, E. A. Mershina, and R. M. Vlasova. "Presurgical brain mapping of language processing with fMRI: state of the art and tendencies." Medical Visualization 26, no. 1 (2022): 48–69. http://dx.doi.org/10.24835/10.24835/1607-0763-1094.

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Presurgical brain mapping of language-eloquent cortex aims to minimize its injury during neurosurgery in patients with brain tumors and drug-resistant epilepsy, and thereby, to preserve their quality of life. Two main goals of language mapping are to identify the localization and lateralization of brain regions involved in language. Gold standards for them are the intraoperative mapping and Wada test, respectively; however, due to some limitations of these techniques, non-invasive preliminary language mapping becomes reasonable. During the last years, fMRI has been widely applied for such purposes. Our literature review focuses on innovations and actual tendencies which spread in the field of language mapping via fMRI in the last decade. State-of-the-art knowledge on brain organization of language, which underpins brain mapping of language processing via fMRI, is briefly described in the article. Contemporary studies of fMRI validity in localization and lateralization of language brain regions are considered. Strategies of presurgical language mapping, such as application of tractography in addition to fMRI, combined analysis of fMRI tasks as well as resting-state fMRI are also discussed. Well-established fMRI tasks for brain mapping of language production and comprehension, as well as new experimental developments in this field, are listed and described.

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44

Kovelman, Ioulia, Stephanie A. Baker, and Laura-Ann Petitto. "Bilingual and Monolingual Brains Compared: A Functional Magnetic Resonance Imaging Investigation of Syntactic Processing and a Possible “Neural Signature” of Bilingualism." Journal of Cognitive Neuroscience 20, no. 1 (2008): 153–69. http://dx.doi.org/10.1162/jocn.2008.20011.

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Does the brain of a bilingual process language differently from that of a monolingual? We compared how bilinguals and monolinguals recruit classic language brain areas in response to a language task and asked whether there is a “neural signature” of bilingualism. Highly proficient and early-exposed adult Spanish-English bilinguals and English monolinguals participated. During functional magnetic resonance imaging (fMRI), participants completed a syntactic “sentence judgment task” [Caplan, D., Alpert, N., & Waters, G. Effects of syntactic structure and propositional number on patterns of regional cerebral blood flow. Journal of Cognitive Neuroscience, 10, 541–552, 1998]. The sentences exploited differences between Spanish and English linguistic properties, allowing us to explore similarities and differences in behavioral and neural responses between bilinguals and monolinguals, and between a bilingual's two languages. If bilinguals' neural processing differs across their two languages, then differential behavioral and neural patterns should be observed in Spanish and English. Results show that behaviorally, in English, bilinguals and monolinguals had the same speed and accuracy, yet, as predicted from the Spanish-English structural differences, bilinguals had a different pattern of performance in Spanish. fMRI analyses revealed that both monolinguals (in one language) and bilinguals (in each language) showed predicted increases in activation in classic language areas (e.g., left inferior frontal cortex, LIFC), with any neural differences between the bilingual's two languages being principled and predictable based on the morphosyntactic differences between Spanish and English. However, an important difference was that bilinguals had a significantly greater increase in the blood oxygenation level-dependent signal in the LIFC (BA 45) when processing English than the English monolinguals. The results provide insight into the decades-old question about the degree of separation of bilinguals' dual-language representation. The differential activation for bilinguals and monolinguals opens the question as to whether there may possibly be a “neural signature” of bilingualism. Differential activation may further provide a fascinating window into the language processing potential not recruited in monolingual brains and reveal the biological extent of the neural architecture underlying all human language.

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Jiang, Jessica, Elia Benhamou, Sheena Waters, et al. "Processing of Degraded Speech in Brain Disorders." Brain Sciences 11, no. 3 (2021): 394. http://dx.doi.org/10.3390/brainsci11030394.

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The speech we hear every day is typically “degraded” by competing sounds and the idiosyncratic vocal characteristics of individual speakers. While the comprehension of “degraded” speech is normally automatic, it depends on dynamic and adaptive processing across distributed neural networks. This presents the brain with an immense computational challenge, making degraded speech processing vulnerable to a range of brain disorders. Therefore, it is likely to be a sensitive marker of neural circuit dysfunction and an index of retained neural plasticity. Considering experimental methods for studying degraded speech and factors that affect its processing in healthy individuals, we review the evidence for altered degraded speech processing in major neurodegenerative diseases, traumatic brain injury and stroke. We develop a predictive coding framework for understanding deficits of degraded speech processing in these disorders, focussing on the “language-led dementias”—the primary progressive aphasias. We conclude by considering prospects for using degraded speech as a probe of language network pathophysiology, a diagnostic tool and a target for therapeutic intervention.

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LAM, SZE-MAN, and JANET H. HSIAO. "Bilingual experience modulates hemispheric lateralization in visual word processing." Bilingualism: Language and Cognition 17, no. 3 (2013): 589–609. http://dx.doi.org/10.1017/s1366728913000734.

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Previous studies showed reduced hemispheric asymmetry in face perception in bilinguals compared with monolinguals, suggesting that hemispheric asymmetry in visual stimulus processing may be modulated by language reading experience. Here we examined whether this phenomenon can also be observed in bilinguals with different language backgrounds. We compared English monolinguals, European–English bilinguals (who know two alphabetic languages), and Chinese–English bilinguals (who have mastered a logographic and an alphabetic language) in an English word sequential matching task. We showed that European–English bilinguals had a stronger right visual field/left hemispheric advantage than the other two groups, suggesting that different language experiences can influence how visual words are processed in the brain. In addition, by using a computational model that implements a theory of hemispheric asymmetry in perception, we showed that this lateralization difference could be accounted for by the difference in participants’ vocabulary size and the difference in word-to-sound mapping between alphabetic and logographic languages.

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47

Hidaka, Souta, Hiroshi Shibata, Michiyo Kurihara, et al. "Effect of second language exposure on brain activity for language processing among preschoolers." Neuroscience Research 73, no. 1 (2012): 73–79. http://dx.doi.org/10.1016/j.neures.2012.02.004.

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48

Ryan, Stephanie Nicole. "Ulf Schütze: Language Learning and the Brain: Lexical Processing in Second Language Acquisition." Journal of Youth and Adolescence 48, no. 10 (2019): 2079–81. http://dx.doi.org/10.1007/s10964-019-01138-4.

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49

Ning, Yan. "Brain activation patterns of English language semantic processing in autistic groups." CNS Spectrums 28, S2 (2023): S5. http://dx.doi.org/10.1017/s1092852923002596.

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BackgroundAutism is a neurodevelopmental disorder, and there are certain differences in the brain activation patterns of English language semantic processing among autistic individuals. The research aims to explore the brain activation patterns of autistic groups during English semantic processing thus to enhance their understanding of language development.Subjects and MethodsThe study used functional Magnetic Resonance Imaging (fMRI) technology, combined with the task paradigm of English word meaning processing, to study the brain activation patterns of individuals with autism. By comparing the differences in brain activation patterns between two groups in word meaning processing tasks, researchers attempted to reveal the specific neural mechanisms involved in language processing in autistic groups. At the same time, SPSS23.0 statistical software was used to process the data.ResultsThrough comparative experiments, the Hamilton Anxiety Rating Scale (HAMA) score and Generalized Self-Efficacy Scale (GSES) score of the experimental group were 7.53 and 31.24 respectively; The HAMA and GSES scores of the control group were 10.84 and 25.81, respectively. The results indicate that the processing of English language word meanings has a brain activation effect on people with autism.Conclusions Research can promote the understanding of the cognitive and neural mechanisms of autism, and contribute to improving daily communication and life quality for autistic groups.AcknowledgementThe Education Department of Hainan Province (No. Hnjgzc2022-121); The Education Department of Hainan Province (No. Hnjgw2022-13).

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Sastry, Rahul A., Aayush Setty, David D. Liu, et al. "Natural language processing augments comorbidity documentation in neurosurgical inpatient admissions." PLOS ONE 19, no. 5 (2024): e0303519. http://dx.doi.org/10.1371/journal.pone.0303519.

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Objective To establish whether or not a natural language processing technique could identify two common inpatient neurosurgical comorbidities using only text reports of inpatient head imaging. Materials and methods A training and testing dataset of reports of 979 CT or MRI scans of the brain for patients admitted to the neurosurgery service of a single hospital in June 2021 or to the Emergency Department between July 1–8, 2021, was identified. A variety of machine learning and deep learning algorithms utilizing natural language processing were trained on the training set (84% of the total cohort) and tested on the remaining images. A subset comparison cohort (n = 76) was then assessed to compare output of the best algorithm against real-life inpatient documentation. Results For “brain compression”, a random forest classifier outperformed other candidate algorithms with an accuracy of 0.81 and area under the curve of 0.90 in the testing dataset. For “brain edema”, a random forest classifier again outperformed other candidate algorithms with an accuracy of 0.92 and AUC of 0.94 in the testing dataset. In the provider comparison dataset, for “brain compression,” the random forest algorithm demonstrated better accuracy (0.76 vs 0.70) and sensitivity (0.73 vs 0.43) than provider documentation. For “brain edema,” the algorithm again demonstrated better accuracy (0.92 vs 0.84) and AUC (0.45 vs 0.09) than provider documentation. Discussion A natural language processing-based machine learning algorithm can reliably and reproducibly identify selected common neurosurgical comorbidities from radiology reports. Conclusion This result may justify the use of machine learning-based decision support to augment provider documentation.

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