scholarly journals Decoding the Neural Representation of Story Meanings across Languages

2017 ◽  
Author(s):  
Morteza Dehghani ◽  
Reihane Boghrati ◽  
Kingson Man ◽  
Joseph Hoover ◽  
Sarah Gimbel ◽  
...  
Keyword(s):  
Language Processing ◽  
Native Speakers ◽  
Semantic Space ◽  
Brain Regions ◽  
Narrative Comprehension ◽  
Neural Representation ◽  
High Dimensional ◽  
Distributed Representations ◽  
Neural Data ◽  
A New Technique

Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower-level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin and Farsi native speakers to native language translations of the same stories during fMRI scanning. Using a new technique in natural language processing, we calculated the distributed representations of these stories (capturing the meaning of the stories in high-dimensional semantic space), and demonstrate that using these representations we can identify the specific story a participant was reading from the neural data. Notably, this was possible even when the distributed representations were calculated using stories in a different language than the participant was reading. Relying on over 44 billion classifications, our results reveal that identification relied on a collection of brain regions most prominently located in the default mode network. These results demonstrate that neuro-semantic encoding of narratives happens at levels higher than individual semantic units and that this encoding is systematic across both individuals and languages.

scholarly journals The neural representation of visually evoked emotion is high-dimensional, categorical, and distributed across transmodal brain regions

2019 ◽  
Author(s):  
Tomoyasu Horikawa ◽  
Alan S. Cowen ◽  
Dacher Keltner ◽  
Yukiyasu Kamitani
Keyword(s):  
Subjective Experience ◽  
Dimensional Space ◽  
Emotional Experience ◽  
Neural Substrates ◽  
Brain Regions ◽  
Neural Representation ◽  
High Dimensional ◽  
High Dimensional Space ◽  
Self Reports ◽  
Affective Dimensions

SummaryCentral to our subjective lives is the experience of different emotions. Recent behavioral work mapping emotional responses to 2185 videos found that people experience upwards of 27 distinct emotions occupying a high-dimensional space, and that emotion categories, more so than affective dimensions (e.g., valence), organize self-reports of subjective experience. Here, we sought to identify the neural substrates of this high-dimensional space of emotional experience using fMRI responses to all 2185 videos. Our analyses demonstrated that (1) dozens of video-evoked emotions were accurately predicted from fMRI patterns in multiple brain regions with different regional configurations for individual emotions, (2) emotion categories better predicted cortical and subcortical responses than affective dimensions, outperforming visual and semantic covariates in transmodal regions, and (3) emotion-related fMRI responses had a cluster-like organization efficiently characterized by distinct categories. These results support an emerging theory of the high-dimensional emotion space, illuminating its neural foundations distributed across transmodal regions.

scholarly journals The Neural Representation of Visually Evoked Emotion Is High-Dimensional, Categorical, and Distributed across Transmodal Brain Regions

iScience ◽  
2020 ◽  
Vol 23 (5) ◽  
pp. 101060 ◽  
Author(s):  
Tomoyasu Horikawa ◽  
Alan S. Cowen ◽  
Dacher Keltner ◽  
Yukiyasu Kamitani
Keyword(s):  
Brain Regions ◽  
Neural Representation ◽  
High Dimensional

scholarly journals Predictive processes during simultaneous interpreting from German into English

Interpreting ◽  
2017 ◽  
Vol 19 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Ena Hodzik ◽  
John N. Williams
Keyword(s):  
Language Processing ◽  
English Language ◽  
Native Speakers ◽  
Transitional Probability ◽  
Semantic Cues ◽  
Simultaneous Interpreting ◽  
Spoken Language Processing ◽  
Advanced Students ◽  
Native Speakers Of English ◽  
Predictive Processes

We report a study on prediction in shadowing and simultaneous interpreting (SI), both considered as forms of real-time, ‘online’ spoken language processing. The study comprised two experiments, focusing on: (i) shadowing of German head-final sentences by 20 advanced students of German, all native speakers of English; (ii) SI of the same sentences into English head-initial sentences by 22 advanced students of German, again native English speakers, and also by 11 trainee and practising interpreters. Latency times for input and production of the target verbs were measured. Drawing on studies of prediction in English-language reading production, we examined two cues to prediction in both experiments: contextual constraints (semantic cues in the context) and transitional probability (the statistical likelihood of words occurring together in the language concerned). While context affected prediction during both shadowing and SI, transitional probability appeared to favour prediction during shadowing but not during SI. This suggests that the two cues operate on different levels of language processing in SI.

scholarly journals On the Role of Bilateral Brain Hypofunction and Abnormal Lateralization of Cortical Information Flow as Neural Underpinnings of Conventional Metaphor Processing Impairment in Schizophrenia: An fMRI and EEG Study

2021 ◽  
Author(s):  
Przemysław Adamczyk ◽  
Martin Jáni ◽  
Tomasz S. Ligeza ◽  
Olga Płonka ◽  
Piotr Błądziński ◽  
...  
Keyword(s):  
Information Flow ◽  
Language Processing ◽  
Time Course ◽  
Figurative Language ◽  
Neural Circuits ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Blood Oxygenation ◽  
Control Group ◽  
Metaphor Processing

AbstractFigurative language processing (e.g. metaphors) is commonly impaired in schizophrenia. In the present study, we investigated the neural activity and propagation of information within neural circuits related to the figurative speech, as a neural substrate of impaired conventional metaphor processing in schizophrenia. The study included 30 schizophrenia outpatients and 30 healthy controls, all of whom were assessed with a functional Magnetic Resonance Imaging (fMRI) and electroencephalography (EEG) punchline-based metaphor comprehension task including literal (neutral), figurative (metaphorical) and nonsense (absurd) endings. The blood oxygenation level-dependent signal was recorded with 3T MRI scanner and direction and strength of cortical information flow in the time course of task processing was estimated with a 64-channel EEG input for directed transfer function. The presented results revealed that the behavioral manifestation of impaired figurative language in schizophrenia is related to the hypofunction in the bilateral fronto-temporo-parietal brain regions (fMRI) and various differences in effective connectivity in the fronto-temporo-parietal circuit (EEG). Schizophrenia outpatients showed an abnormal pattern of connectivity during metaphor processing which was related to bilateral (but more pronounced at the left hemisphere) hypoactivation of the brain. Moreover, we found reversed lateralization patterns, i.e. a rightward-shifted pattern during metaphor processing in schizophrenia compared to the control group. In conclusion, the presented findings revealed that the impairment of the conventional metaphor processing in schizophrenia is related to the bilateral brain hypofunction, which supports the evidence on reversed lateralization of the language neural network and the existence of compensatory recruitment of alternative neural circuits in schizophrenia.

scholarly journals Generation of Cross-Lingual Word Vectors for Low-Resourced Languages Using Deep Learning and Topological Metrics in a Data-Efficient Way

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1372
Author(s):  
Sanjanasri JP ◽  
Vijay Krishna Menon ◽  
Soman KP ◽  
Rajendran S ◽  
Agnieszka Wolk
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Semantic Space ◽  
Semantic Interpretation ◽  
Learning Approaches ◽  
Qualitative Comparison ◽  
Bilingual Dictionary ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Cross Lingual

Linguists have been focused on a qualitative comparison of the semantics from different languages. Evaluation of the semantic interpretation among disparate language pairs like English and Tamil is an even more formidable task than for Slavic languages. The concept of word embedding in Natural Language Processing (NLP) has enabled a felicitous opportunity to quantify linguistic semantics. Multi-lingual tasks can be performed by projecting the word embeddings of one language onto the semantic space of the other. This research presents a suite of data-efficient deep learning approaches to deduce the transfer function from the embedding space of English to that of Tamil, deploying three popular embedding algorithms: Word2Vec, GloVe and FastText. A novel evaluation paradigm was devised for the generation of embeddings to assess their effectiveness, using the original embeddings as ground truths. Transferability across other target languages of the proposed model was assessed via pre-trained Word2Vec embeddings from Hindi and Chinese languages. We empirically prove that with a bilingual dictionary of a thousand words and a corresponding small monolingual target (Tamil) corpus, useful embeddings can be generated by transfer learning from a well-trained source (English) embedding. Furthermore, we demonstrate the usability of generated target embeddings in a few NLP use-case tasks, such as text summarization, part-of-speech (POS) tagging, and bilingual dictionary induction (BDI), bearing in mind that those are not the only possible applications.

Second language processing of English past tense morphology: The role of working memory

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Filiz Rızaoğlu ◽  
Ayşe Gürel
Keyword(s):  
Working Memory ◽  
Second Language ◽  
Language Processing ◽  
Native Speakers ◽  
Negative Relationship ◽  
Masked Priming ◽  
Morphological Processing ◽  
Target Language ◽  
Past Tense ◽  
L2 Learners

AbstractThis study examines, via a masked priming task, the processing of English regular and irregular past tense morphology in proficient second language (L2) learners and native speakers in relation to working memory capacity (WMC), as measured by the Automated Reading Span (ARSPAN) and Operation Span (AOSPAN) tasks. The findings revealed quantitative group differences in the form of slower reaction times (RTs) in the L2-English group. While no correlation was found between the morphological processing patterns and WMC in either group, there was a negative relationship between English and Turkish ARSPAN scores and the speed of word recognition in the L2 group. Overall, comparable decompositional processing patterns found in both groups suggest that, like native speakers, high-proficiency L2 learners are sensitive to the morphological structure of the target language.

scholarly journals Comparison of Activation in the Prefrontal Cortex of Native Speakers of Mandarin by Ability of Japanese as a Second Language Using a Novel Speaking Task

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 412
Author(s):  
Li Cong ◽  
Hideki Miyaguchi ◽  
Chinami Ishizuki
Keyword(s):  
Second Language ◽  
Prefrontal Cortex ◽  
Cognitive Load ◽  
Near Infrared ◽  
Native Speakers ◽  
Brain Activation ◽  
Brain Regions ◽  
Strong Inhibition ◽  
Functional Near Infrared Spectroscopy ◽  
High Level

Evidence shows that second language (L2) learning affects cognitive function. Here in this work, we compared brain activation in native speakers of Mandarin (L1) who speak Japanese (L2) between and within two groups (high and low L2 ability) to determine the effect of L2 ability in L1 and L2 speaking tasks, and to map brain regions involved in both tasks. The brain activation during task performance was determined using prefrontal cortex blood flow as a proxy, measured by functional near-infrared spectroscopy (fNIRS). People with low L2 ability showed much more brain activation when speaking L2 than when speaking L1. People with high L2 ability showed high-level brain activation when speaking either L2 or L1. Almost the same high-level brain activation was observed in both ability groups when speaking L2. The high level of activation in people with high L2 ability when speaking either L2 or L1 suggested strong inhibition of the non-spoken language. A wider area of brain activation in people with low compared with high L2 ability when speaking L2 is considered to be attributed to the cognitive load involved in code-switching L1 to L2 with strong inhibition of L1 and the cognitive load involved in using L2.

scholarly journals Neural Components of Reading Revealed by Distributed and Symbolic Computational Models

2020 ◽  
Vol 1 (4) ◽  
pp. 381-401
Author(s):  
Ryan Staples ◽  
William W. Graves
Keyword(s):  
Neural Activity ◽  
Neural Coding ◽  
Computational Models ◽  
Ann Model ◽  
Distributed Representations ◽  
Neural Data ◽  
Cognitive Components ◽  
Components Of Reading ◽  
Artificial Neural Network Ann ◽  
Models Of Reading

Determining how the cognitive components of reading—orthographic, phonological, and semantic representations—are instantiated in the brain has been a long-standing goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit nonsymbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling–to–sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded to neural activity. However, the ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

scholarly journals Functional Neuroanatomy of Contextual Acquisition of Concrete and Abstract Words

2009 ◽  
Vol 21 (11) ◽  
pp. 2154-2171 ◽  
Author(s):  
Anna Mestres-Missé ◽  
Thomas F. Münte ◽  
Antoni Rodriguez-Fornells
Keyword(s):  
Native Speakers ◽  
Brain Regions ◽  
Functional Neuroanatomy ◽  
Linguistic Context ◽  
Abstract Concepts ◽  
New Words ◽  
Simulation Paradigm ◽  
Abstract Words ◽  
The Brain ◽  
Concrete Words

The meaning of a novel word can be acquired by extracting it from linguistic context. Here we simulated word learning of new words associated to concrete and abstract concepts in a variant of the human simulation paradigm that provided linguistic context information in order to characterize the brain systems involved. Native speakers of Spanish read pairs of sentences in order to derive the meaning of a new word that appeared in the terminal position of the sentences. fMRI revealed that learning the meaning associated to concrete and abstract new words was qualitatively different and recruited similar brain regions as the processing of real concrete and abstract words. In particular, learning of new concrete words selectively boosted the activation of the ventral anterior fusiform gyrus, a region driven by imageability, which has previously been implicated in the processing of concrete words.

scholarly journals Fundamental Dynamical Modes Underlying Human Brain Synchronization

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Catalina Alvarado-Rojas ◽  
Michel Le Van Quyen
Keyword(s):  
Large Scale ◽  
Brain Regions ◽  
Electrode Placement ◽  
High Dimensional ◽  
Sleep Cycle ◽  
Epileptic Patients ◽  
Brain States ◽  
Intracranial Recordings ◽  
Characteristic Dynamic

Little is known about the long-term dynamics of widely interacting cortical and subcortical networks during the wake-sleep cycle. Using large-scale intracranial recordings of epileptic patients during seizure-free periods, we investigated local- and long-range synchronization between multiple brain regions over several days. For such high-dimensional data, summary information is required for understanding and modelling the underlying dynamics. Here, we suggest that a compact yet useful representation is given by a state space based on the first principal components. Using this representation, we report, with a remarkable similarity across the patients with different locations of electrode placement, that the seemingly complex patterns of brain synchrony during the wake-sleep cycle can be represented by a small number of characteristic dynamic modes. In this space, transitions between behavioral states occur through specific trajectories from one mode to another. These findings suggest that, at a coarse level of temporal resolution, the different brain states are correlated with several dominant synchrony patterns which are successively activated across wake-sleep states.

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