Neurobiology of Language最新文献_第3页

A Comparison of White Matter Brain Differences in Monolingual and Highly Proficient Multilingual Speakers 单语使用者与精通多种语言者大脑白质差异比较

IF 3.2

Neurobiology of Language Pub Date : 2024-04-05 DOI: 10.1162/nol_a_00144

Ludmila Midrigan-Ciochina, Kayla P. Vodacek, Cristina Sewell, David P. Corina

{"title":"A Comparison of White Matter Brain Differences in Monolingual and Highly Proficient Multilingual Speakers","authors":"Ludmila Midrigan-Ciochina, Kayla P. Vodacek, Cristina Sewell, David P. Corina","doi":"10.1162/nol_a_00144","DOIUrl":"https://doi.org/10.1162/nol_a_00144","url":null,"abstract":"\u0000 Language processing relies on the communication between brain regions that is achieved through several white matter tracts, part of the dorsal, ventral, and medial pathways involved in language processing and control (Friederici & Gierhan, 2013; Hickok & Poeppel, 2007, Coggins et al., 2004; Luk et al., 2011). While changes in white matter tract morphology have been reported as a function of second language learning in bilinguals, little is known about changes that may be present in multi-language users. Here we investigate white matter morphometry in a group of highly proficient multilinguals, (individuals with proficiency in four or more languages), compared to a group of monolinguals. White matter morphometry was quantified using a fixel-based analysis (Raffelt et al., 2015, 2017; Tournier et al., 2007). Higher fiber cross-section (FC) and lower fiber density (FD) values were observed for the multilinguals, in the dorsal pathways (superior longitudinal fasciculus and arcuate fasciculus) and the ventral pathway, including the inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, and the uncinate fasciculus. Segments of the corpus callosum, the fornix, and the cortico-spinal tract showed decreases in all three morphometry measures for multilinguals. The findings suggest differential efficiencies in neural communication between domain-specific language regions and domain-general cognitive processes underlying multilingual language use. We discuss the results in relation to the bilingual Anterior to Posterior and Subcortical Shift (BAPSS) hypothesis (Grundy et al., 2017) and the Dynamic Restructuring Model (DRM; Pliatsikas, 2020).","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140739256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Selective Neural Entrainment Reveals Hierarchical Tuning to Linguistic Regularities in Reading 选择性神经诱导揭示了阅读中语言规律性的分层调谐

IF 3.2

Neurobiology of Language Pub Date : 2024-04-05 DOI: 10.1162/nol_a_00145

M. De Rosa, L. Vignali, Anna D'Urso, Maria Ktori, Roberto Bottini, Davide Crepaldi

{"title":"Selective Neural Entrainment Reveals Hierarchical Tuning to Linguistic Regularities in Reading","authors":"M. De Rosa, L. Vignali, Anna D'Urso, Maria Ktori, Roberto Bottini, Davide Crepaldi","doi":"10.1162/nol_a_00145","DOIUrl":"https://doi.org/10.1162/nol_a_00145","url":null,"abstract":"\u0000 Reading is both a visual and a linguistic task, and as such it relies on both general-purpose, visual mechanisms and more abstract, meaning-oriented processes. Disentangling the roles of these resources is of paramount importance in reading research. The present study capitalizes on the coupling of Fast Periodic Visual Stimulation (FPVS; Rossion, 2014) and MEG recordings to address this issue and investigate the role of dierent kinds of visual and linguistic units in the visual word identification system. We compared strings of pseudo-characters (BACS; C. Vidal & Chetail, 2017); strings of consonants (e.g,. sfcl); readable, but unattested strings (e.g., amsi); frequent, but non-meaningful chunks (e.g., idge); suffixes (e.g., ment); and words (e.g., vibe); and looked for discrimination responses with a particular focus on the ventral, occipito-temporal regions. The results revealed sensitivity to alphabetic, readable, familiar and lexical stimuli. Interestingly, there was no discrimination between suffixes and equally frequent, but meaningless endings, thus highlighting a lack of sensitivity to semantics. Taken together, the data suggest that the visual word identification system, at least in its early processing stages, is particularly tuned to form-based regularities, most likely reflecting its reliance on general-purpose, statistical learning mechanisms that are a core feature of the visual system as implemented in the ventral stream.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Relationship between resting state functional connectivity and reading-related behavioural measures in 69 adults 69 名成人的静息状态功能连接与阅读相关行为测量之间的关系

IF 3.2

Neurobiology of Language Pub Date : 2024-04-05 DOI: 10.1162/nol_a_00146

J. Bathelt, Kathy Rastle, J. S. H. Taylor

{"title":"Relationship between resting state functional connectivity and reading-related behavioural measures in 69 adults","authors":"J. Bathelt, Kathy Rastle, J. S. H. Taylor","doi":"10.1162/nol_a_00146","DOIUrl":"https://doi.org/10.1162/nol_a_00146","url":null,"abstract":"\u0000 In computational models of reading, written words can be read using print-to-sound and/or print-to-meaning pathways. Neuroimaging data associate dorsal stream regions (left posterior occipitotemporal cortex, intraparietal cortex (IPC), dorsal inferior frontal gyrus, (dIFG)) with the print-to-sound pathway and ventral stream regions (left anterior fusiform gyrus (aFG), middle temporal gyrus (MTG)) with the print-to-meaning pathway. In 69 typical adults, we investigated whether resting state functional connectivity (RSFC) between the visual word form area (VWFA) and dorsal and ventral regions correlated with phonological (nonword reading, nonword repetition, spoonerisms), lexical-semantic (vocabulary, sensitivity to morpheme units in reading), and general literacy (word reading, spelling) skills. VWFA activity was temporally correlated with activity in both dorsal and ventral reading regions. In pre-registered whole-brain analyses, spoonerisms performance was positively correlated with RSFC between the VWFA and left dorsal regions (dIFG, superior/intra PC). In exploratory region-of-interest analyses, VWFA-dIFG connectivity was also positively correlated with nonword repetition, spelling, and vocabulary. Connectivity between the VWFA and ventral stream regions was not associated with performance on any behavioural measure, either in whole-brain or region-of-interest analyses. Our results suggest that tasks such as spoonerisms and spellings, which are both complex (i.e. involve multiple sub-processes) and have high between-subject variability, provide greater opportunity for observing resting-state brain-behaviour associations. However, the complexity of these tasks limits the conclusions we can draw about the specific mechanisms that drive these associations. Future research would benefit from constructing latent variables from multiple tasks tapping the same reading subprocess.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140736820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Localizing Syntactic Composition with Left-Corner Recurrent Neural Network Grammars. 用左角递归神经网络语法定位句法组成

IF 3.2

Neurobiology of Language Pub Date : 2024-04-01 eCollection Date: 2024-01-01 DOI: 10.1162/nol_a_00118

Yushi Sugimoto, Ryo Yoshida, Hyeonjeong Jeong, Masatoshi Koizumi, Jonathan R Brennan, Yohei Oseki

{"title":"Localizing Syntactic Composition with Left-Corner Recurrent Neural Network Grammars.","authors":"Yushi Sugimoto, Ryo Yoshida, Hyeonjeong Jeong, Masatoshi Koizumi, Jonathan R Brennan, Yohei Oseki","doi":"10.1162/nol_a_00118","DOIUrl":"10.1162/nol_a_00118","url":null,"abstract":"In computational neurolinguistics, it has been demonstrated that hierarchical models such as recurrent neural network grammars (RNNGs), which jointly generate word sequences and their syntactic structures via the syntactic composition, better explained human brain activity than sequential models such as long short-term memory networks (LSTMs). However, the vanilla RNNG has employed the top-down parsing strategy, which has been pointed out in the psycholinguistics literature as suboptimal especially for head-final/left-branching languages, and alternatively the left-corner parsing strategy has been proposed as the psychologically plausible parsing strategy. In this article, building on this line of inquiry, we investigate not only whether hierarchical models like RNNGs better explain human brain activity than sequential models like LSTMs, but also which parsing strategy is more neurobiologically plausible, by developing a novel fMRI corpus where participants read newspaper articles in a head-final/left-branching language, namely Japanese, through the naturalistic fMRI experiment. The results revealed that left-corner RNNGs outperformed both LSTMs and top-down RNNGs in the left inferior frontal and temporal-parietal regions, suggesting that there are certain brain regions that localize the syntactic composition with the left-corner parsing strategy.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11025653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48524613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dissociable Neural Mechanisms for Human Inference Processing Predicted by Static and Contextual Language Models. 静态和上下文语言模型预测的人类推理过程的可分离神经机制

IF 3.2

Neurobiology of Language Pub Date : 2024-04-01 eCollection Date: 2024-01-01 DOI: 10.1162/nol_a_00090

Takahisa Uchida, Nicolas Lair, Hiroshi Ishiguro, Peter Ford Dominey

{"title":"Dissociable Neural Mechanisms for Human Inference Processing Predicted by Static and Contextual Language Models.","authors":"Takahisa Uchida, Nicolas Lair, Hiroshi Ishiguro, Peter Ford Dominey","doi":"10.1162/nol_a_00090","DOIUrl":"10.1162/nol_a_00090","url":null,"abstract":"Language models (LMs) continue to reveal non-trivial relations to human language performance and the underlying neurophysiology. Recent research has characterized how word embeddings from an LM can be used to generate integrated discourse representations in order to perform inference on events. The current research investigates how such event knowledge may be coded in distinct manners in different classes of LMs and how this maps onto different forms of human inference processing. To do so, we investigate inference on events using two well-documented human experimental protocols from Metusalem et al. (2012) and McKoon and Ratcliff (1986), compared with two protocols for simpler semantic processing. Interestingly, this reveals a dissociation in the relation between local semantics versus event-inference depending on the LM. In a series of experiments, we observed that for the static LMs (word2vec/GloVe), there was a clear dissociation in the relation between semantics and inference for the two inference tasks. In contrast, for the contextual LMs (BERT/RoBERTa), we observed a correlation between semantic and inference processing for both inference tasks. The experimental results suggest that inference as measured by Metusalem and McKoon rely on dissociable processes. While the static models are able to perform Metusalem inference, only the contextual models succeed in McKoon inference. Interestingly, these dissociable processes may be linked to well-characterized automatic versus strategic inference processes in the psychological literature. This allows us to make predictions about dissociable neurophysiological markers that should be found during human inference processing with these tasks.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11025649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46269600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Neural Network Language Models Predict Human Brain Responses to Language Even After a Developmentally Realistic Amount of Training. 人工神经网络语言模型可预测人脑对语言的反应，即使是在经过符合发展实际的大量训练之后。

IF 3.2

Neurobiology of Language Pub Date : 2024-04-01 eCollection Date: 2024-01-01 DOI: 10.1162/nol_a_00137

Eghbal A Hosseini, Martin Schrimpf, Yian Zhang, Samuel Bowman, Noga Zaslavsky, Evelina Fedorenko

{"title":"Artificial Neural Network Language Models Predict Human Brain Responses to Language Even After a Developmentally Realistic Amount of Training.","authors":"Eghbal A Hosseini, Martin Schrimpf, Yian Zhang, Samuel Bowman, Noga Zaslavsky, Evelina Fedorenko","doi":"10.1162/nol_a_00137","DOIUrl":"https://doi.org/10.1162/nol_a_00137","url":null,"abstract":"Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use two complementary approaches to ask how the models' ability to capture human fMRI responses to sentences is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion words against an fMRI benchmark. We consider the 100-million-word model to be developmentally plausible in terms of the amount of training data given that this amount is similar to what children are estimated to be exposed to during the first 10 years of life. Second, we test the performance of a GPT-2 model trained on a 9-billion-token dataset to reach state-of-the-art next-word prediction performance on the human benchmark at different stages during training. Across both approaches, we find that (i) the models trained on a developmentally plausible amount of data already achieve near-maximal performance in capturing fMRI responses to sentences. Further, (ii) lower perplexity-a measure of next-word prediction performance-is associated with stronger alignment with human data, suggesting that models that have received enough training to achieve sufficiently high next-word prediction performance also acquire representations of sentences that are predictive of human fMRI responses. In tandem, these findings establish that although some training is necessary for the models' predictive ability, a developmentally realistic amount of training (∼100 million words) may suffice.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11025646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tracking Lexical and Semantic Prediction Error Underlying the N400 Using Artificial Neural Network Models of Sentence Processing. 利用句子处理的人工神经网络模型追踪 N400 的词汇和语义预测误差。

IF 3.2

Neurobiology of Language Pub Date : 2024-04-01 eCollection Date: 2024-01-01 DOI: 10.1162/nol_a_00134

Alessandro Lopopolo, Milena Rabovsky

{"title":"Tracking Lexical and Semantic Prediction Error Underlying the N400 Using Artificial Neural Network Models of Sentence Processing.","authors":"Alessandro Lopopolo, Milena Rabovsky","doi":"10.1162/nol_a_00134","DOIUrl":"https://doi.org/10.1162/nol_a_00134","url":null,"abstract":"Recent research has shown that the internal dynamics of an artificial neural network model of sentence comprehension displayed a similar pattern to the amplitude of the N400 in several conditions known to modulate this event-related potential. These results led Rabovsky et al. (2018) to suggest that the N400 might reflect change in an implicit predictive representation of meaning corresponding to semantic prediction error. This explanation stands as an alternative to the hypothesis that the N400 reflects lexical prediction error as estimated by word surprisal (Frank et al., 2015). In the present study, we directly model the amplitude of the N400 elicited during naturalistic sentence processing by using as predictor the update of the distributed representation of sentence meaning generated by a sentence gestalt model (McClelland et al., 1989) trained on a large-scale text corpus. This enables a quantitative prediction of N400 amplitudes based on a cognitively motivated model, as well as quantitative comparison of this model to alternative models of the N400. Specifically, we compare the update measure from the sentence gestalt model to surprisal estimated by a comparable language model trained on next-word prediction. Our results suggest that both sentence gestalt update and surprisal predict aspects of N400 amplitudes. Thus, we argue that N400 amplitudes might reflect two distinct but probably closely related sub-processes that contribute to the processing of a sentence.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11025650/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140868474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational Language Modeling and the Promise of In Silico Experimentation. 计算机语言建模和计算机实验的前景

IF 3.2

Neurobiology of Language Pub Date : 2024-04-01 eCollection Date: 2024-01-01 DOI: 10.1162/nol_a_00101

Shailee Jain, Vy A Vo, Leila Wehbe, Alexander G Huth

引用次数: 0

Strong Prediction: Language Model Surprisal Explains Multiple N400 Effects. 强预测：语言模型惊奇地解释了N400的多种影响

IF 3.2

Neurobiology of Language Pub Date : 2024-04-01 eCollection Date: 2024-01-01 DOI: 10.1162/nol_a_00105

James A Michaelov, Megan D Bardolph, Cyma K Van Petten, Benjamin K Bergen, Seana Coulson

{"title":"Strong Prediction: Language Model Surprisal Explains Multiple N400 Effects.","authors":"James A Michaelov, Megan D Bardolph, Cyma K Van Petten, Benjamin K Bergen, Seana Coulson","doi":"10.1162/nol_a_00105","DOIUrl":"10.1162/nol_a_00105","url":null,"abstract":"Theoretical accounts of the N400 are divided as to whether the amplitude of the N400 response to a stimulus reflects the extent to which the stimulus was predicted, the extent to which the stimulus is semantically similar to its preceding context, or both. We use state-of-the-art machine learning tools to investigate which of these three accounts is best supported by the evidence. GPT-3, a neural language model trained to compute the conditional probability of any word based on the words that precede it, was used to operationalize contextual predictability. In particular, we used an information-theoretic construct known as surprisal (the negative logarithm of the conditional probability). Contextual semantic similarity was operationalized by using two high-quality co-occurrence-derived vector-based meaning representations for words: GloVe and fastText. The cosine between the vector representation of the sentence frame and final word was used to derive contextual cosine similarity estimates. A series of regression models were constructed, where these variables, along with cloze probability and plausibility ratings, were used to predict single trial N400 amplitudes recorded from healthy adults as they read sentences whose final word varied in its predictability, plausibility, and semantic relationship to the likeliest sentence completion. Statistical model comparison indicated GPT-3 surprisal provided the best account of N400 amplitude and suggested that apparently disparate N400 effects of expectancy, plausibility, and contextual semantic similarity can be reduced to variation in the predictability of words. The results are argued to support predictive coding in the human language network.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11025652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45647353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictive Coding or Just Feature Discovery? An Alternative Account of Why Language Models Fit Brain Data. 预测编码还是仅仅是特征发现？为什么语言模型适合大脑数据的另一种解释

IF 3.6

Neurobiology of Language Pub Date : 2024-04-01 eCollection Date: 2024-01-01 DOI: 10.1162/nol_a_00087

Richard Antonello, Alexander Huth

引用次数: 0