scholarly journals Semantic representations during language comprehension are affected by context

2021 ◽  
Author(s):  
Fatma Deniz ◽  
Christine Tseng ◽  
Leila Wehbe ◽  
Jack L Gallant
Keyword(s):  
Natural Language ◽  
Semantic Information ◽  
Brain Activity ◽  
Word Meaning ◽  
Brain Responses ◽  
Neuroimaging Data ◽  
The Brain ◽  
Model Approach ◽  
Prefrontal Cortices

The meaning of words in natural language depends crucially on context. However, most neuroimaging studies of word meaning use isolated words and isolated sentences with little context. Because the brain may process natural language differently from how it processes simplified stimuli, there is a pressing need to determine whether prior results on word meaning generalize to natural language. We investigated this issue by directly comparing the brain representation of semantic information across four conditions that vary in context. fMRI was used to record human brain activity while four subjects (two female) read words presented in four different conditions: narratives (Narratives), isolated sentences (Sentences), blocks of semantically similar words (Semantic Blocks), and isolated words (Single Words). Using a voxelwise encoding model approach, we find two clear and consistent effects of increasing context. First, stimuli with more context (Narratives, Sentences) evoke brain responses with substantially higher SNR across bilateral visual, temporal, parietal, and prefrontal cortices compared to stimuli with little context (Semantic Blocks, Single Words). Second, increasing context increases the representation of semantic information across bilateral temporal, parietal, and prefrontal cortices at the group level. However, in individual subjects, only natural language stimuli (Narratives) consistently evoke widespread representation of semantic information across the cortical surface. These results show that context has large effects on both the quality of neuroimaging data and on the representation of meaning in the brain, and they imply that the results of neuroimaging studies that use stimuli with little context may not generalize well to the natural regime.

scholarly journals Semantic Grounding of Novel Spoken Words in the Primary Visual Cortex

2021 ◽  
Vol 15 ◽  
Author(s):  
Max Garagnani ◽  
Evgeniya Kirilina ◽  
Friedemann Pulvermüller
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Semantic Information ◽  
Word Meaning ◽  
Familiar Object ◽  
The Novel ◽  
Brain Responses ◽  
Spoken Words ◽  
First Time ◽  
The Brain

Embodied theories of grounded semantics postulate that, when word meaning is first acquired, a link is established between symbol (word form) and corresponding semantic information present in modality-specific—including primary—sensorimotor cortices of the brain. Direct experimental evidence documenting the emergence of such a link (i.e., showing that presentation of a previously unknown, meaningless word sound induces, after learning, category-specific reactivation of relevant primary sensory or motor brain areas), however, is still missing. Here, we present new neuroimaging results that provide such evidence. We taught participants aspects of the referential meaning of previously unknown, senseless novel spoken words (such as “Shruba” or “Flipe”) by associating them with either a familiar action or a familiar object. After training, we used functional magnetic resonance imaging to analyze the participants’ brain responses to the new speech items. We found that hearing the newly learnt object-related word sounds selectively triggered activity in the primary visual cortex, as well as secondary and higher visual areas.These results for the first time directly document the formation of a link between the novel, previously meaningless spoken items and corresponding semantic information in primary sensory areas in a category-specific manner, providing experimental support for perceptual accounts of word-meaning acquisition in the brain.

Classification Algorithms for EEG-Based Brain-Computer Interface

2019 ◽  
pp. 52-73
Author(s):  
Sravanth Kumar Ramakuri ◽  
Chinmay Chakraboirty ◽  
Anudeep Peddi ◽  
Bharat Gupta
Keyword(s):  
Brain Activity ◽  
Biomedical Signal Processing ◽  
Computer Interface ◽  
Eeg Signals ◽  
Large Sets ◽  
Eeg Data ◽  
Important Measurement ◽  
The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

scholarly journals Recent advances in functional neuroimaging analysis for cognitive neuroscience

2018 ◽  
Vol 2 ◽  
pp. 239821281775272 ◽  
Author(s):  
Nitin Williams ◽  
Richard N. Henson
Keyword(s):  
Functional Neuroimaging ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Recent Analysis ◽  
Temporal Data ◽  
New Methods ◽  
Neuroimaging Data ◽  
The Rich ◽  
Scientific Questions ◽  
The Brain

Functional magnetic resonance imaging and electro-/magneto-encephalography are some of the main neuroimaging technologies used by cognitive neuroscientists to study how the brain works. However, the methods for analysing the rich spatial and temporal data they provide are constantly evolving, and these new methods in turn allow new scientific questions to be asked about the brain. In this brief review, we highlight a handful of recent analysis developments that promise to further advance our knowledge about the working of the brain. These include (1) multivariate approaches to decoding the content of brain activity, (2) time-varying approaches to characterising states of brain connectivity, (3) neurobiological modelling of neuroimaging data, and (4) standardisation and big data initiatives.

scholarly journals GPT-2’s activations predict the degree of semantic comprehension in the human brain

2021 ◽  
Author(s):  
Charlotte Caucheteux ◽  
Alexandre Gramfort ◽  
Jean-Rémi King
Keyword(s):  
Brain Activity ◽  
Planum Temporale ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Brain Responses ◽  
The Brain ◽  
Semantic Comprehension ◽  
Empirical Framework ◽  
Superior Frontal Cortex ◽  
Language Network

Language transformers, like GPT-2, have demonstrated remarkable abilities to process text, and now constitute the backbone of deep translation, summarization and dialogue algorithms. However, whether these models actually understand language is highly controversial. Here, we show that the representations of GPT-2 not only map onto the brain responses to spoken stories, but also predict the extent to which subjects understand the narratives. To this end, we analyze 101 subjects recorded with functional Magnetic Resonance Imaging while listening to 70 min of short stories. We then fit a linear model to predict brain activity from GPT-2 activations, and correlate this mapping with subjects’ comprehension scores as assessed for each story. The results show that GPT-2’s brain predictions significantly correlate with semantic comprehension. These effects are bilaterally distributed in the language network and peak with a correlation above 30% in the infero-frontal and medio-temporal gyri as well as in the superior frontal cortex, the planum temporale and the precuneus. Overall, this study provides an empirical framework to probe and dissect semantic comprehension in brains and deep learning algorithms.

scholarly journals Local and distributed cortical markers of effort expenditure during sustained goal pursuit

2020 ◽  
Author(s):  
Lauren M. Patrick ◽  
Kevin M. Anderson ◽  
Avram J. Holmes
Keyword(s):  
Goal Attainment ◽  
Brain Activity ◽  
Goal Pursuit ◽  
Physical Effort ◽  
Course Of Action ◽  
Brain Responses ◽  
Subjective Valuation ◽  
Medial Pfc ◽  
Past Effort ◽  
The Brain

AbstractThe adaptive adjustment of behavior in pursuit of desired goals is critical for survival. To accomplish this complex feat, individuals must weigh the potential benefits of a given course of action against time, energy, and resource costs. Prior research in this domain has greatly advanced understanding of the cortico-striatal circuits that support the anticipation and receipt of desired outcomes, characterizing core aspects of subjective valuation at discrete points in time. However, motivated goal pursuit is not a static or cost neutral process and the brain mechanisms that underlie individual differences in the dynamic updating of effort expenditure across time remain unclear. Here, 38 healthy right-handed participants underwent functional MRI (fMRI) while completing a novel paradigm to examine their willingness to exert physical effort over a prolonged trial, either to obtain monetary rewards or avoid punishments. During sustained goal pursuit, medial prefrontal cortex (mPFC) response scaled with trial-to-trial differences in effort expenditure as a function of both monetary condition and eventual task earnings. Multivariate pattern analysis (MVPA) searchlights were used to examine relations linking prior trial-level effort expenditure to subsequent brain responses to feedback. At reward feedback, whole-brain searchlights identified signals reflecting past effort expenditure in dorsal and ventral mPFC, encompassing broad swaths of frontoparietal and dorsal attention networks. These results suggest a core role for mPFC in scaling effort expenditure during sustained goal pursuit, with the subsequent tracking of effort costs following successful goal attainment extending to incorporate distributed brain networks that support executive functioning and externally oriented attention.Significance StatementHistorically, much of the research on subjective valuation has focused on discrete points in time. Here, we examine brain responses associated with willingness to exert physical effort during the sustained pursuit of desired goals. Our analyses reveal a distributed pattern of brain activity encompassing aspects of ventral mPFC that tracks with trial-level variability in effort expenditure. Indicating that the brain represents echoes of effort at the point of feedback, searchlight analyses revealed signals associated with past effort expenditure in broad swaths of dorsal and medial PFC. These data have important implications for the study of how the brain’s valuation mechanisms contend with the complexity of real-world dynamic environments with relevance for the study of behavior across health and disease.

scholarly journals Insights into Early Word Comprehension: Tracking the Neural Representations of Word Semantics in Infants.

2021 ◽  
Author(s):  
Rohan Saha ◽  
Jennifer Campbell ◽  
Janet F. Werker ◽  
Alona Fyshe
Keyword(s):  
Brain Activity ◽  
Word Meaning ◽  
Age Groups ◽  
Event Related Potential ◽  
Neural Representation ◽  
Machine Learning Techniques ◽  
Semantic Representations ◽  
Word Comprehension ◽  
Neural Representations ◽  
The Brain

Infants start developing rudimentary language skills and can start understanding simple words well before their first birthday. This development has also been shown primarily using Event Related Potential (ERP) techniques to find evidence of word comprehension in the infant brain. While these works validate the presence of semantic representations of words (word meaning) in infants, they do not tell us about the mental processes involved in the manifestation of these semantic representations or the content of the representations. To this end, we use a decoding approach where we employ machine learning techniques on Electroencephalography (EEG) data to predict the semantic representations of words found in the brain activity of infants. We perform multiple analyses to explore word semantic representations in two groups of infants (9-month-old and 12-month-old). Our analyses show significantly above chance decodability of overall word semantics, word animacy, and word phonetics. As we analyze brain activity, we observe that participants in both age groups show signs of word comprehension immediately after word onset, marked by our model's significantly above chance word prediction accuracy. We also observed strong neural representations of word phonetics in the brain data for both age groups, some likely correlated to word decoding accuracy and others not. Lastly, we discover that the neural representations of word semantics are similar in both infant age groups. Our results on word semantics, phonetics, and animacy decodability, give us insights into the evolution of neural representation of word meaning in infants.

Electroencephalographic Evidence on the Strategies of Adaptation to the Factors of Monotony

2009 ◽  
Vol 12 (1) ◽  
pp. 32-45 ◽  
Author(s):  
Elena V. Aslanyan ◽  
Valery N. Kiroy
Keyword(s):  
Power Plants ◽  
Work Performance ◽  
Brain Activity ◽  
The State ◽  
High Quality ◽  
High Lability ◽  
Occupational Selection ◽  
The Brain

In a series of studies, in which 19 apparently healthy male volunteers participated, on the basis of a comparative analysis of the bioelectric brain activity and work performance, it is shown that two strategies of adaptation to the factors of monotony are possible. One of them is based on the maintenance of a high quality of activity even at the price of a considerable reduction in the functional state of the brain; the second is based on the maintenance of the functional status of the brain even at the expense of the short-term loss of control over realizable performance. The factor conditioning the long term inability to support continual high quality of performance under the conditions of monotony is a high lability in nervous processes. The resistance to the effects of the factors of monotony is connected, on the other hand, with the low lability of nervous processes with a certain predominance of excitatory processes over inhibiting processes. The electrographic correlates of the development of the state of monotony represent an increase in the EEG of an alert person of the slow spectra (theta and alpha), and also beta-2 waves, as well as a reduction in the intrahemispheric coherence of alpha-waves. These results can be used for the development of control systems for the state of the operators who work in conditions of monotony (pilots, the operators of electric trains, the operators of power plants, including atomic power plants, and others), as well as in the occupational selection of individuals for jobs involving work under such conditions.

scholarly journals Dynamics of Muslim consumers’ behavior toward Halal products

2019 ◽  
Vol 14 (4) ◽  
pp. 689-708 ◽  
Author(s):  
Osama Sam Al-Kwifi ◽  
Allam Abu Farha ◽  
Zafar U. Ahmed
Keyword(s):  
Brain Activity ◽  
Research Model ◽  
Religious Perspective ◽  
Content Type ◽  
Emotional Sensitivity ◽  
Brain Responses ◽  
Consumer Segment ◽  
Unique Approach ◽  
Cooked Meat ◽  
The Brain

Purpose Since Islamic markets are growing substantially, there is an urgent need to gain a better understanding of how Muslim consumers perceive products from a religious perspective. The purpose of this paper is to investigate the brain responses of Muslim consumers to Halal and non-Halal products using a functional magnetic resonance imaging (fMRI) technology. Design/methodology/approach The research model is a simplified version of the theory of planned behavior. The initial experiment began by asking participants to divide a set of images into two groups: Halal and non-Halal products. The fMRI experiment uses a blocked design approach to capture brain activities resulting from presenting the two groups of images to participants, and to record the strength of their attitudes toward purchasing the products. Findings Across all participants, the level of brain activation in the ventromedial prefrontal cortex increased significantly when Halal images were presented to them. The same results emerged when the Halal images showed raw and cooked meat. The variations in the results may be due to the high emotional sensitivity of Muslim consumers to using religious products. Research limitations/implications This study uses a unique approach to monitor brain activity to confirm that consumers from specific market segments respond differently to market products based on their internal beliefs. Findings from this study provide evidence that marketing managers targeting Muslim markets should consider the sensitivity of presenting products in ways that reflect religious principles, in order to gain higher acceptance in this market segment. Originality/value Although the literature reports considerable research on Muslim consumers’ behavior, most of the previous studies utilize conventional data collection approaches to target broad segments of consumers by using traditional products. This paper is the first to track the reactions of the Muslim consumer segment to specific types of market products.

scholarly journals EEG frequency tagging reveals neural entrainment to people moving in synchrony

2020 ◽  
Author(s):  
Emiel Cracco ◽  
Haeeun Lee ◽  
Goedele van Belle ◽  
Lisa Quenon ◽  
Patrick Haggard ◽  
...  
Keyword(s):  
Biological Motion ◽  
Brain Activity ◽  
Motion Processing ◽  
Body Postures ◽  
Brain Responses ◽  
Biological Motion Perception ◽  
Hierarchical Levels ◽  
Orientation Experiment ◽  
Neural Entrainment ◽  
The Brain

AbstractHumans and other animals have evolved to act in groups, but how does the brain distinguish multiple people moving in group from multiple people moving independently? Across three experiments, we test whether biological motion perception depends on the spatiotemporal relationships among people moving together. In Experiment 1, we apply EEG frequency tagging to apparent biological motion and show that fluently ordered sequences of body postures drive brain activity at three hierarchical levels of biological motion processing: image, body sequence, and movement. We then show that movement-, but not body- or image-related brain responses are enhanced when observing four agents moving in synchrony. Neural entrainment was strongest for fluently moving synchronous groups (Experiment 2), displayed in upright orientation (Experiment 3). Our findings show that the brain preferentially entrains to the collective movement of human agents, deploying perceptual organization principles of synchrony and common fate for the purpose of social perception.

scholarly journals Applying stochastic spike train theory for high-accuracy MEG/EEG

2019 ◽  
Author(s):  
Niels Trusbak Haumann ◽  
Minna Huotilainen ◽  
Peter Vuust ◽  
Elvira Brattico
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Brain Activity ◽  
Component Analysis ◽  
High Accuracy ◽  
Spike Timing ◽  
Clinical Diagnostics ◽  
Evoked Response ◽  
Brain Responses ◽  
The Brain

AbstractThe accuracy of electroencephalography (EEG) and magnetoencephalography (MEG) is challenged by overlapping sources from within the brain. This lack of accuracy is a severe limitation to the possibilities and reliability of modern stimulation protocols in basic research and clinical diagnostics. As a solution, we here introduce a theory of stochastic neuronal spike timing probability densities for describing the large-scale spiking activity in neural networks, and a novel spike density component analysis (SCA) method for isolating specific neural sources. Three studies are conducted based on 564 cases of evoked responses to auditory stimuli from 94 human subjects each measured with 60 EEG electrodes and 306 MEG sensors. In the first study we show that the large-scale spike timing (but not non-encephalographic artifacts) in MEG/EEG waveforms can be modeled with Gaussian probability density functions with high accuracy (median 99.7%-99.9% variance explained), while gamma and sine functions fail describing the MEG and EEG waveforms. In the second study we confirm that SCA can isolate a specific evoked response of interest. Our findings indicate that the mismatch negativity (MMN) response is accurately isolated with SCA, while principal component analysis (PCA) fails supressing interference from overlapping brain activity, e.g. from P3a and alpha waves, and independent component analysis (ICA) distorts the evoked response. Finally, we confirm that SCA accurately reveals inter-individual variation in evoked brain responses, by replicating findings relating individual traits with MMN variations. The findings of this paper suggest that the commonly overlapping neural sources in single-subject or patient data can be more accurately separated by applying the introduced theory of large-scale spike timing and method of SCA in comparison to PCA and ICA.Significance statementElectroencephalography (EEG) and magnetoencelopraphy (MEG) are among the most applied non-invasive brain recording methods in humans. They are the only methods to measure brain function directly and in time resolutions smaller than seconds. However, in modern research and clinical diagnostics the brain responses of interest cannot be isolated, because of interfering signals of other ongoing brain activity. For the first time, we introduce a theory and method for mathematically describing and isolating overlapping brain signals, which are based on prior intracranial in vivo research on brain cells in monkey and human neural networks. Three studies mutually support our theory and suggest that a new level of accuracy in MEG/EEG can achieved by applying the procedures presented in this paper.

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