Dissociation of Brain Activity Related to Syntactic and Semantic Aspects of Language

1993 ◽  
Vol 5 (3) ◽  
pp. 335-344 ◽  
Author(s):  
Thomas F. Münte ◽  
Hans-Jochen Heinze ◽  
George R Mangun
Keyword(s):  
Language Processing ◽  
Early Stage ◽  
Brain Activity ◽  
Brain Potentials ◽  
Semantic Condition ◽  
Distributional Information ◽  
Preceding Word ◽  
N400 Component ◽  
Young Subjects ◽  
Theoretical Issues

In psycholinguistic research, there has been considerable interest in understanding the interactions of difFerent types of linguistic information during language processing. For example, does syntactic information interact with semantic or pragmatic information at an early stage of language processing, or only at later stages in order to resolve ambiguities of language? Developing reliable measures of language processes such as syntax and semantics is important to address many of these theoretical issues in psycholinguistics. In the present study, event-related brain potentials (ERPs) were recorded from healthy young subjects while they read pairs of words presented one word at a time. The ERPs for the second word of each pair were compared as a function of whether the preceding word was or was not (1) semantically related (i.e., synonyms; “semantic condition”) or (2) grammatically correct (“syntactic condition”). In the semantic condition the ERPs obtained to words preceded by nonsemantically related words elicited an N400 component that was maximal over centroparietal scalp regions. In contrast, in the syntactic condition the ERPs obtained to words preceded by grammatically incorrect articles or pronouns yielded a negativity with a later onset, and a frontopolar, left hemisphere scalp maximum. This replicates our previous findings of a syntactic negativity in a word pair design that was performed in the German language. Further, the present data provide scalp distributional information, which suggests that the syntactic negativity represents brain processes that are dissociable from the centroparietal N400 component. Thus, these findings provide strong evidence for a separate negative polarity ERP component that indexes syntactic aspects of language processing.

scholarly journals Isn't It Ironic? An Electrophysiological Exploration of Figurative Language Processing

2011 ◽  
Vol 23 (2) ◽  
pp. 277-293 ◽  
Author(s):  
Stefanie Regel ◽  
Thomas C. Gunter ◽  
Angela D. Friederici
Keyword(s):  
Language Processing ◽  
Cognitive Processes ◽  
Time Course ◽  
Figurative Language ◽  
Semantic Integration ◽  
Task Demands ◽  
Brain Potentials ◽  
Late Positivity ◽  
Figurative Language Processing ◽  
N400 Component

Although the neurocognitive processes underlying the comprehension of figurative language, especially metaphors and idioms, have been studied extensively, less is known about the processing of irony. In two experiments using event-related brain potentials (ERPs), we examined the types of cognitive processes involved in the comprehension of ironic and literal sentences and their relative time course. The experiments varied in modality (auditory, visual), task demands (comprehension task vs. passive reading), and probability of stimulus occurrence. ERPs consistently revealed a large late positivity (i.e., P600 component) in the absence of an N400 component for irony compared to equivalent literal sentences independent of modality. This P600 was shown to be unaffected by the factors task demands and probability of occurrence. Taken together, the findings suggest that the observed P600 is related to irony processing, and might be a reflection of pragmatic interpretation processes. During the comprehension of irony, no semantic integration difficulty arises (absence of N400), but late inferential processes appear to be necessary for understanding ironic meanings (presence of P600). This finding calls for a revision of current models of figurative language processing.

scholarly journals Traces of Meaning Itself: Encoding distributional word vectors in brain activity

2019 ◽  
Author(s):  
Jona Sassenhagen ◽  
Christian J. Fiebach
Keyword(s):  
Language Processing ◽  
Word Processing ◽  
Time Window ◽  
Brain Activity ◽  
Word Meaning ◽  
Human Mind ◽  
Semantic Knowledge ◽  
Brain Potentials ◽  
Word Onset ◽  
Vector Representations

AbstractHow is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, i.e., the ease vs. difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English, German) read isolated words, we encode and decode word vectors taken from the family of prediction-based word2vec algorithms. We find that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.

scholarly journals Traces of Meaning Itself: Encoding Distributional Word Vectors in Brain Activity

2020 ◽  
Vol 1 (1) ◽  
pp. 54-76 ◽  
Author(s):  
Jona Sassenhagen ◽  
Christian J. Fiebach
Keyword(s):  
Language Processing ◽  
Word Processing ◽  
Time Window ◽  
Brain Activity ◽  
Word Meaning ◽  
Human Mind ◽  
Semantic Knowledge ◽  
Brain Potentials ◽  
Word Onset ◽  
Vector Representations

How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, that is, the ease versus difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English and German) read isolated words, we encoded and decoded word vectors taken from the family of prediction-based Word2vec algorithms. We found that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.

scholarly journals Ereigniskorrelierte Potenziale (EKPs)

2012 ◽  
Vol 40 (1) ◽  
pp. 68-96
Author(s):  
Heiner Drenhaus ◽  
Peter beim Graben
Keyword(s):  
Language Processing ◽  
Speech Processing ◽  
Brain Activity ◽  
Average Technique ◽  
Laboratory Equipment ◽  
Brain Potentials ◽  
Technical Requirements ◽  
Ereigniskorrelierte Potenziale ◽  
Classical Average ◽  
Processing Steps

AbstractIn this article we give a short introduction to the online method of event-related (brain) potentials (ERPs) and their importance for our understanding of language structure and grammar. This methodology places high demands on (technical) requirements for laboratory equipment as well as on the skills of the investigator. However, the high costs are relatively balanced compared to the advantages of this experimental method. By using ERPs, it becomes possible to monitor the electrophysiological brain activity associated with speech processing in real time (millisecond by millisecond) and to draw conclusions on human language processing and the human parser.First, we present briefly how this method works and how ERPs can be classified (Section 1 and 2). In the following, we show that the ERP method can be used to study the processing of e. g. semantic, pragmatic and syntactic information (Section 3). Crucial for our discussion will be the interpretation of the so-called ERP components and their connection and importance for psycholinguistics and theoretical linguistics. In our presentation, we emphasize, that the electrophysiological brain activity in relation to specific (e. g. linguistic) stimuli can be used to identify distinct processes, which give a deeper insight into the different processing steps of language. At the end of this article (Section 4), we present some results from ERP studies of German negative-polar elements. Additionally, we highlight the advantage and benefits of an alternative method to analyze ERP data compared to the more ‘classical’ average technique.

Skipping Breakfast Affects the Early Steps of Cognitive Processing

2019 ◽  
Vol 33 (2) ◽  
pp. 109-118
Author(s):  
Andrés Antonio González-Garrido ◽  
Jacobo José Brofman-Epelbaum ◽  
Fabiola Reveca Gómez-Velázquez ◽  
Sebastián Agustín Balart-Sánchez ◽  
Julieta Ramos-Loyo
Keyword(s):  
Working Memory ◽  
Cognitive Processing ◽  
Task Difficulty ◽  
Brain Activity ◽  
Reaction Times ◽  
Brain Potentials ◽  
Brain Functioning ◽  
Attentional Processes ◽  
Electrical Brain Activity ◽  
Skipping Breakfast

Abstract. It has been generally accepted that skipping breakfast adversely affects cognition, mainly disturbing the attentional processes. However, the effects of short-term fasting upon brain functioning are still unclear. We aimed to evaluate the effect of skipping breakfast on cognitive processing by studying the electrical brain activity of young healthy individuals while performing several working memory tasks. Accordingly, the behavioral results and event-related brain potentials (ERPs) of 20 healthy university students (10 males) were obtained and compared through analysis of variances (ANOVAs), during the performance of three n-back working memory (WM) tasks in two morning sessions on both normal (after breakfast) and 12-hour fasting conditions. Significantly fewer correct responses were achieved during fasting, mainly affecting the higher WM load task. In addition, there were prolonged reaction times with increased task difficulty, regardless of breakfast intake. ERP showed a significant voltage decrement for N200 and P300 during fasting, while the amplitude of P200 notably increased. The results suggest skipping breakfast disturbs earlier cognitive processing steps, particularly attention allocation, early decoding in working memory, and stimulus evaluation, and this effect increases with task difficulty.

On the Nature of Gender Categorization

2012 ◽  
Vol 43 (1) ◽  
pp. 14-27 ◽  
Author(s):  
Silvia Tomelleri ◽  
Luigi Castelli
Keyword(s):  
Early Stage ◽  
Processing Stage ◽  
Brain Potentials ◽  
Ongoing Process ◽  
Gender Categorization

In the present paper, relying on event-related brain potentials (ERPs), we investigated the automatic nature of gender categorization focusing on different stages of the ongoing process. In particular, we explored the degree to which gender categorization occurs automatically by manipulating the semantic vs. nonsemantic processing goals requested by the task (Study 1) and the complexity of the task itself (Study 2). Results of Study 1 highlighted the automatic nature of categorization at an early (N170) and on a later processing stage (P300). Findings of Study 2 showed that at an early stage categorization was automatically driven by the ease of extraction of category-based knowledge from faces while, at a later stage, categorization was more influenced by situational constrains.

scholarly journals A starring role for inference in the neurocognition of visual narratives

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Neil Cohn
Keyword(s):  
Language Processing ◽  
Mental Model ◽  
Brain Potentials ◽  
Visual Narratives ◽  
Inferential Processing

AbstractResearch in verbal and visual narratives has often emphasized backward-looking inferences, where absent information is subsequently inferred. However, comics use conventions like star-shaped “action stars” where a reader knows events are undepicted at that moment, rather than omitted entirely. We contrasted the event-related brain potentials (ERPs) to visual narratives depicting an explicit event, an action star, or a “noise” panel of scrambled lines. Both action stars and noise panels evoked large N400s compared to explicit-events (300–500 ms), but action stars and noise panels then differed in their later effects (500–900 ms). Action stars elicited sustained negativities and P600s, which could indicate further interpretive processes and integration of meaning into a mental model, while noise panels evoked late frontal positivities possibly indexing that they were improbable narrative units. Nevertheless, panels following action stars and noise panels both evoked late sustained negativities, implying further inferential processing. Inference in visual narratives thus uses cascading mechanisms resembling those in language processing that differ based on the inferential techniques.

scholarly journals EEG Fractal Analysis Reflects Brain Impairment after Stroke

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 592
Author(s):  
Maria Rubega ◽  
Emanuela Formaggio ◽  
Franco Molteni ◽  
Eleonora Guanziroli ◽  
Roberto Di Marco ◽  
...  
Keyword(s):  
Early Stage ◽  
Brain Activity ◽  
Phase 1 ◽  
Behavioral Changes ◽  
Stroke Survivors ◽  
Eeg Signals ◽  
Novel Treatments ◽  
Fractal Measures ◽  
Underlying Mechanisms ◽  
Random Fluctuations

Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week after stroke), and 35 stroke survivors during the early subacute phase (from 8 days to 32 days and after ∼2 months after stroke): We compared resting-state EEG fractal changes using fractal measures (i.e., Higuchi Index, Tortuosity) with 11 healthy controls. Both Higuchi index and Tortuosity values were significantly lower after a stroke throughout the acute and early subacute stage compared to healthy subjects, reflecting a brain activity which is significantly less complex. These indices may be promising metrics to track behavioral changes in the very early stage after stroke. Our findings might contribute to the neurorehabilitation quest in identifying reliable biomarkers for a better tailoring of rehabilitation pathways.

Development of AI-based method to detect the subtle ECG deviations from the population ECG norm

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
I Chaikovsky ◽  
A Popov ◽  
D Fogel ◽  
A Kazmirchyk
Keyword(s):  
Early Stage ◽  
Primary Source ◽  
Feature Space ◽  
Prognostic Information ◽  
National Budget ◽  
Young Subjects ◽  
Public Grant ◽  
Normal Ecgs ◽  
Healthy Persons ◽  
Population Norm

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Academy of Science of Ukraine Background Electrocardiogram (ECG) is still the primary source for the diagnostic and prognostic information about cardiovascular diseases. The concept of "normal ECG" parameters is crucial for the reliable diagnosis, since it provides reference for the ECG under examination. With the development of new methods and tools for ECG feature extraction and classification based on artificial intelligence (AI), it becomes possible to identify subtle changes in the heart activity to detect  possible abnormalities at the early stage.  The challenge of this work is to identify the deviations in  ECG of clinically healthy persons  from the conditional "population" norm . Methods The normal ECG is described as a feature vector composed of the time-magnitude parameters of signal-averaged ECG (SAECG). To define the subjects that possibly have variations from the "population" norm, the outlier detection approach is proposed: first the cloud of the vectors , constructed from the set of normal ECG"s , obtained from  young, clinically similar healthy persons  was created in feature space. Then, a particular ECG is considered deviant and requires the attention of the clinician when it is considered an outlier of the cloud of normal ECGs. In the experiment, SAECGs from the group of 139 young subjects (male, age 18-28  years) with no reported cardiovascular problems are used to extract 34 features from SAECG leads (magnitudes and durations of ECG waves, duration of ECG segments, etc.). ECGs were routinely previewed by qualified physicians, and no obvious anomalies were noticed. The Isolation Forest anomaly detection method is used with variable numbers of trees and different contamination parameters.  Results The ratio of outliers were changed from 5 to 10% (7-12 subjects) with various numbers of estimator trees. Seven outlier SAECGs were repeatedly appearing for various settings. Out of these, 4 subjects were the oldest persons in group examined , and 3 others had a rare ventricular premature beats during routine ECG examination. Conclusion The proposed method is promising for application in routine and express ECG tests since it is able to quantify the subtle deviations from the normal ECG group.

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