Neurobiology of LanguagePub Date : 2024-06-03eCollection Date: 2024-01-01DOI: 10.1162/nol_a_00139
Sara D Beach, Ding-Lan Tang, Swathi Kiran, Caroline A Niziolek
{"title":"Pars Opercularis Underlies Efferent Predictions and Successful Auditory Feedback Processing in Speech: Evidence From Left-Hemisphere Stroke.","authors":"Sara D Beach, Ding-Lan Tang, Swathi Kiran, Caroline A Niziolek","doi":"10.1162/nol_a_00139","DOIUrl":"10.1162/nol_a_00139","url":null,"abstract":"<p><p>Hearing one's own speech allows for acoustic self-monitoring in real time. Left-hemisphere motor planning regions are thought to give rise to efferent predictions that can be compared to true feedback in sensory cortices, resulting in neural suppression commensurate with the degree of overlap between predicted and actual sensations. Sensory prediction errors thus serve as a possible mechanism of detection of deviant speech sounds, which can then feed back into corrective action, allowing for online control of speech acoustics. The goal of this study was to assess the integrity of this detection-correction circuit in persons with aphasia (PWA) whose left-hemisphere lesions may limit their ability to control variability in speech output. We recorded magnetoencephalography (MEG) while 15 PWA and age-matched controls spoke monosyllabic words and listened to playback of their utterances. From this, we measured speaking-induced suppression of the M100 neural response and related it to lesion profiles and speech behavior. Both speaking-induced suppression and cortical sensitivity to deviance were preserved at the group level in PWA. PWA with more spared tissue in pars opercularis had greater left-hemisphere neural suppression and greater behavioral correction of acoustically deviant pronunciations, whereas sparing of superior temporal gyrus was not related to neural suppression or acoustic behavior. In turn, PWA who made greater corrections had fewer overt speech errors in the MEG task. Thus, the motor planning regions that generate the efferent prediction are integral to performing corrections when that prediction is violated.</p>","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":"5 2","pages":"454-483"},"PeriodicalIF":3.6,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11192514/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141443466","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}
Neurobiology of LanguagePub Date : 2024-04-01eCollection Date: 2024-01-01DOI: 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":"<p><p>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 <i>some</i> training is necessary for the models' predictive ability, a developmentally realistic amount of training (∼100 million words) may suffice.</p>","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":"5 1","pages":"43-63"},"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}
Neurobiology of LanguagePub Date : 2024-04-01eCollection Date: 2024-01-01DOI: 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":"<p><p>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.</p>","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":"5 1","pages":"136-166"},"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}
Ana Zappa, Deirdre Bolger, Jean-Marie Pergandi, Raphael Fargier, Daniel Mestre, Cheryl Frenck-Mestre
{"title":"The neural correlates of embodied L2 learning: Does embodied L2 verb\u0000 learning affect representation and retention?","authors":"Ana Zappa, Deirdre Bolger, Jean-Marie Pergandi, Raphael Fargier, Daniel Mestre, Cheryl Frenck-Mestre","doi":"10.1162/nol_a_00132","DOIUrl":"https://doi.org/10.1162/nol_a_00132","url":null,"abstract":"\u0000 We investigated how naturalistic actions in a highly immersive, multimodal, interactive 3D virtual reality (VR) environment may enhance word encoding by recording EEG in a pre/post-test learning paradigm. While behavior data has shown that coupling word encoding with gestures congruent with word meaning enhances learning, the neural underpinnings of this effect have yet to be elucidated. We coupled EEG recording with VR to examine whether “embodied learning” improves learning and creates linguistic representations that produce greater motor resonance. Participants learned action verbs in an L2 in two different conditions: Specific action (observing and performing congruent actions on virtual objects) and Pointing (observing actions and pointing to virtual objects). Pre and post-training participants performed a Match-mismatch task as we measured EEG (variation in the N400 response as a function of match between observed actions and auditory verbs) and a Passive listening task while we measured motor activation (mu (8-13 Hz) and beta band (13-30Hz) desynchronization during auditory verb processing) during verb processing. Contrary to our expectations, post-training results revealed neither semantic nor motor effects in either group when considered independently of learning success. Behavioral results showed a great deal of variability in learning success. When considering performance, Low performance learners showed no semantic effect and High performance learners exhibited an N400 effect for Mismatch vs Match trails post-training, independent of the type of learning. Taken as a whole, our results suggest that embodied processes can play an important role in L2 learning.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":"52 12","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381818","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}
{"title":"Neurobiological causal models of language processing","authors":"H. Fitz, P. Hagoort, K. Petersson","doi":"10.1162/nol_a_00133","DOIUrl":"https://doi.org/10.1162/nol_a_00133","url":null,"abstract":"\u0000 The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap. A neurobiological causal model is a mechanistic description of language processing that is grounded in, and constrained by, the characteristics of the neurobiological substrate. It intends to model the generators of language behavior at the level of implementational causality. We describe key features and neurobiological component parts from which causal models can be built and provide guidelines on how to implement them in model simulations. Then we outline how this approach can shed new light on the core computational machinery for language, the long-term storage of words in the mental lexicon and combinatorial processing in sentence comprehension. In contrast to cognitive theories of behavior, causal models are formulated in the ‘machine language’ of neurobiology which is universal to human cognition. We argue that neurobiological causal modeling should be pursued in addition to existing approaches. Eventually, this approach will allow us to develop an explicit computational neurobiology of language.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":"16 5","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139383258","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}
Patti Adank, Georgios P. D. Argyropoulos, K. Armeni, Christoph Aurnhammer, Nilgoun Bahar, Jana Basnakova, Laura Batterink, Idan Blank, Lindsay Bowman, Jonathan Brennan, Trevor Brothers, Adam Buchwald, Chiara Cantiani, Stefano Cappa, Micaela Chan, Luyao Chen, Yuchun Chen, A. Chrabaszcz, Laurent Cohen, H. Coslett, Jacqueline Cummine, Anila D ’ Mello, A. Daliri, Nicola Del, Maschio Andrew, Tesla DeMarco, D. D. Ouden, Michele T. Diaz, Anthony Steven Dick, Guosheng Ding, Nai Ding, Irene Echeverria-Altuna, Mark Eckert, Allyson Ettinger, Z. Eviatar, Heather Flowers, Robert Frank, Stefan Frank, Jon Gauthier, Giulia Gennari, Fatemeh Geranmayeh, Laura Giglio
{"title":"Neurobiology of Language: Volume 4 Reviewers List","authors":"Patti Adank, Georgios P. D. Argyropoulos, K. Armeni, Christoph Aurnhammer, Nilgoun Bahar, Jana Basnakova, Laura Batterink, Idan Blank, Lindsay Bowman, Jonathan Brennan, Trevor Brothers, Adam Buchwald, Chiara Cantiani, Stefano Cappa, Micaela Chan, Luyao Chen, Yuchun Chen, A. Chrabaszcz, Laurent Cohen, H. Coslett, Jacqueline Cummine, Anila D ’ Mello, A. Daliri, Nicola Del, Maschio Andrew, Tesla DeMarco, D. D. Ouden, Michele T. Diaz, Anthony Steven Dick, Guosheng Ding, Nai Ding, Irene Echeverria-Altuna, Mark Eckert, Allyson Ettinger, Z. Eviatar, Heather Flowers, Robert Frank, Stefan Frank, Jon Gauthier, Giulia Gennari, Fatemeh Geranmayeh, Laura Giglio","doi":"10.1162/nol_e_00130","DOIUrl":"https://doi.org/10.1162/nol_e_00130","url":null,"abstract":"","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":"291 ","pages":"637 - 638"},"PeriodicalIF":3.2,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138991772","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}
Hannah Mechtenberg, Christopher C. Heffner, Emily B. Myers, Sara Guediche
{"title":"The cerebellum is sensitive to the lexical properties of words during spoken language comprehension","authors":"Hannah Mechtenberg, Christopher C. Heffner, Emily B. Myers, Sara Guediche","doi":"10.1162/nol_a_00126","DOIUrl":"https://doi.org/10.1162/nol_a_00126","url":null,"abstract":"Abstract Over the past few decades, research into the function of the cerebellum has expanded far beyond the motor domain. A growing number of studies are probing the role of specific cerebellar subregions, such as Crus I and Crus II, in higher-order cognitive functions including receptive language processing. In the current fMRI study, we show evidence for the cerebellum’s sensitivity to variation in two well-studied psycholinguistic properties of words–lexical frequency and phonological neighborhood density–during passive, continuous listening of a podcast. To determine whether, and how, activity in the cerebellum correlates with these lexical properties, we modeled each word separately using an amplitude-modulated regressor, time-locked to the onset of each word. At the group level, significant effects of both lexical properties landed in expected cerebellar subregions: Crus I and Crus II. The BOLD signal correlated with variation in both lexical properties; patterns consistent with both language-specific and domain-general mechanisms. Activation patterns at the individual level also showed that effects of phonological neighborhood and lexical frequency landed in Crus I and Crus II as the most probable sites, though there was activation seen in other lobules (especially for frequency). Although the exact cerebellar mechanisms used during speech and language processing are not yet evident, these findings highlight the cerebellum’s role in word-level processing during continuous listening.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":"363 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135392301","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}
{"title":"Information-Restricted Neural Language Models Reveal Different Brain Regions' Sensitivity to Semantics, Syntax and Context","authors":"Alexandre Pasquiou, Yair Lakretz, Bertrand Thirion, Christophe Pallier","doi":"10.1162/nol_a_00125","DOIUrl":"https://doi.org/10.1162/nol_a_00125","url":null,"abstract":"Abstract A fundamental question in neurolinguistics concerns the brain regions involved in syntactic and semantic processing during speech comprehension, both at the lexical (word processing) and supra-lexical levels (sentence and discourse processing). To what extent are these regions separated or intertwined? To address this question, we introduce a novel approach exploiting neural language models to generate high-dimensional feature sets that separately encode semantic and syntactic information. More precisely, we train a lexical language model, Glove, and a supra-lexical language model, GPT-2, on a text corpus from which we selectively removed either syntactic or semantic information. We then assess to what extent the features derived from these information-restricted models are still able to predict the fMRI time courses of humans listening to naturalistic text. Furthermore, to determine the windows of integration of brain regions involved in supra-lexical processing, we manipulate the size of contextual information provided to GPT-2. The analyses show that, while most brain regions involved in language comprehension are sensitive to both syntactic and semantic features, the relative magnitudes of these effects vary across these regions. Moreover, regions that are best fitted by semantic or syntactic features are more spatially dissociated in the left hemisphere than in the right one, and the right hemisphere shows sensitivity to longer contexts than the left. The novelty of our approach lies in the ability to control for the information encoded in the models’ embeddings by manipulating the training set. These “information-restricted” models complement previous studies that used language models to probe the neural bases of language, and shed new light on its spatial organization.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":"22 3‐4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135432016","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}
Neurobiology of LanguagePub Date : 2023-10-31eCollection Date: 2023-01-01DOI: 10.1162/nol_a_00115
Alexandra C Brito, Deborah F Levy, Sarah M Schneck, Jillian L Entrup, Caitlin F Onuscheck, Marianne Casilio, Michael de Riesthal, L Taylor Davis, Stephen M Wilson
{"title":"Leukoaraiosis Is Not Associated With Recovery From Aphasia in the First Year After Stroke.","authors":"Alexandra C Brito, Deborah F Levy, Sarah M Schneck, Jillian L Entrup, Caitlin F Onuscheck, Marianne Casilio, Michael de Riesthal, L Taylor Davis, Stephen M Wilson","doi":"10.1162/nol_a_00115","DOIUrl":"10.1162/nol_a_00115","url":null,"abstract":"<p><p>After a stroke, individuals with aphasia often recover to a certain extent over time. This recovery process may be dependent on the health of surviving brain regions. Leukoaraiosis (white matter hyperintensities on MRI reflecting cerebral small vessel disease) is one indication of compromised brain health and is associated with cognitive and motor impairment. Previous studies have suggested that leukoaraiosis may be a clinically relevant predictor of aphasia outcomes and recovery, although findings have been inconsistent. We investigated the relationship between leukoaraiosis and aphasia in the first year after stroke. We recruited 267 patients with acute left hemispheric stroke and coincident fluid attenuated inversion recovery MRI. Patients were evaluated for aphasia within 5 days of stroke, and 174 patients presented with aphasia acutely. Of these, 84 patients were evaluated at ∼3 months post-stroke or later to assess longer-term speech and language outcomes. Multivariable regression models were fit to the data to identify any relationships between leukoaraiosis and initial aphasia severity, extent of recovery, or longer-term aphasia severity. We found that leukoaraiosis was present to varying degrees in 90% of patients. However, leukoaraiosis did not predict initial aphasia severity, aphasia recovery, or longer-term aphasia severity. The lack of any relationship between leukoaraiosis severity and aphasia recovery may reflect the anatomical distribution of cerebral small vessel disease, which is largely medial to the white matter pathways that are critical for speech and language function.</p>","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":"4 4","pages":"536-549"},"PeriodicalIF":3.2,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72015569","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}
Marissa M Lee, Lauren M McGrath, Catherine J Stoodley
{"title":"Cerebellar neuromodulation impacts reading fluency in young adults","authors":"Marissa M Lee, Lauren M McGrath, Catherine J Stoodley","doi":"10.1162/nol_a_00124","DOIUrl":"https://doi.org/10.1162/nol_a_00124","url":null,"abstract":"Abstract The cerebellum is traditionally associated with the control of coordinated movement, but ample evidence suggests that the cerebellum also supports cognitive processing. Consistent with this, right-lateralized posterolateral cerebellar regions are engaged during a range of reading and reading-related tasks, but the specific role of the cerebellum during reading tasks is not clear. Based on the cerebellar contribution to automatizing movement, it has been hypothesized that the cerebellum is specifically involved in rapid, fluent reading. We aimed to determine whether the right posterolateral cerebellum is a specific modulator of reading fluency or whether cerebellar modulation is broader, also impacting reading accuracy, rapid automatized naming, and general processing speed. To do this, we examined the effect of transcranial direct current stimulation (tDCS) targeting the right posterolateral cerebellum (lobules VI/VII) on single-word reading fluency, reading accuracy, rapid automatized naming, and processing speed. Young adults with typical reading development (n = 25; 15 female sex assigned at birth, 10 male sex assigned at birth, aged 18–28 years [M = 19.92 ± 2.04 years]) completed the reading and cognitive measures after 20 min of 2 mA anodal (excitatory), cathodal (inhibitory), or sham tDCS in a within-subjects design. Linear mixed effects models indicated that cathodal tDCS decreased single-word reading fluency scores (d = −0.36, p &lt; 0.05) but did not significantly affect single-word reading accuracy, rapid automatized naming, or general processing speed measures. Our results suggest that the right posterolateral cerebellum is involved in reading fluency, consistent with a broader role of the cerebellum in fast, fluent cognition.","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135666580","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}
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