The Neurobiology of Lexical and Sentential Negation

2020 ◽  
pp. 739-756
Author(s):  
Liuba Papeo ◽  
Manuel de Vega
Keyword(s):  
Word Processing ◽  
Semantic Processing ◽  
Word Meaning ◽  
Brain Network ◽  
Neural Representation ◽  
Language Understanding ◽  
Sentential Negation ◽  
Neurophysiological Mechanisms ◽  
Pragmatic Aspects ◽  
The Brain

Is negation inherently harder than affirmation? How does negation change the neural representation of a word meaning? Recent studies considering pragmatic aspects of language, largely neglected in early studies on negation, have challenged the view that negation involves an extra processing stage to modify, or recode, an affirmative representation initially activated. In consequence, researchers have sought to determine the earliest effects of negation on the neural activity associated with language understanding. Behavioral, neuroimaging, and electrophysiological research have highlighted three circumstances. First, negation changes the neural representation of a word meaning from the earliest stages of word processing. Second, negation reduces or abolishes the activity in brain areas representing the word in the scope of negation. Third, the brain network for response inhibition is recruited during processing negation. Composing these results into a unitary framework, a neurobiological model of negation is proposed that integrates the neurophysiological mechanisms of lexical-semantic processing and inhibition.

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.

scholarly journals The brain network in support of social semantic accumulation

2021 ◽  
Author(s):  
Guangyao Zhang ◽  
Yangwen Xu ◽  
Meimei Zhang ◽  
Shaonan Wang ◽  
Nan Lin
Keyword(s):  
Semantic Processing ◽  
Semantic Network ◽  
Meta Analysis ◽  
Word List ◽  
Brain Network ◽  
Complex Materials ◽  
Semantic Richness ◽  
The Social ◽  
Social Meanings ◽  
The Brain

Abstract Some studies have indicated that a specific ‘social semantic network’ represents the social meanings of words. However, studies of the comprehension of complex materials, such as sentences and narratives, have indicated that the same network supports the online accumulation of connected semantic information. In this study, we examined the hypothesis that this network does not simply represent the social meanings of words but also accumulates connected social meanings from texts. We defined the social semantic network by conducting a meta-analysis of previous studies on social semantic processing and then examined the effects of social semantic accumulation using a functional Magnetic Resonance Imaging (fMRI) experiment. Two important findings were obtained. First, the social semantic network showed a stronger social semantic effect in sentence and narrative reading than in word list reading, indicating the amplitude of social semantic activation can be accumulated in the network. Second, the activation of the social semantic network in sentence and narrative reading can be better explained by the holistic social-semantic-richness rating scores of the stimuli than by those of the constitutive words, indicating the social semantic contents can be integrated in the network. These two findings convergently indicate that the social semantic network supports the accumulation of connected social meanings.

The Default Computation of Negated Meanings

2016 ◽  
Vol 28 (12) ◽  
pp. 1980-1986 ◽  
Author(s):  
Liuba Papeo ◽  
Jean-Rémy Hochmann ◽  
Lorella Battelli
Keyword(s):  
Semantic Processing ◽  
Neural Representation ◽  
Main Hypothesis ◽  
Initial Stage ◽  
Step Model ◽  
Positive Representation ◽  
Motor Excitability ◽  
Inhibition Experiment ◽  
Physiological Markers ◽  
The Brain

Negation is a fundamental component of human reasoning and language. Yet, current neurocognitive models, conceived to account for the cortical representation of meanings (e.g., writing), hardly accommodate the representation of negated meanings (not writing). One main hypothesis, known as the two-step model, proposes that, for negated meanings, the corresponding positive representation is first fully activated and then modified to reflect negation. Recast in neurobiological terms, this model predicts that, in the initial stage of semantic processing, the neural representation of a stimulus' meaning is indistinguishable from the neural representation of that meaning following negation. Although previous work has shown that pragmatic and task manipulations can favor or hinder a two-step processing, we just do not know how the brain processes an utterance as simple as “I am not writing.” We implemented two methodologies based on chronometric TMS to measure motor excitability (Experiment 1) and inhibition (Experiment 2) as physiological markers of semantic access to action-related meanings. We used elementary sentences (Adverb + Verb) and a passive reading task. For the first time, we defined action word-related motor activity in terms of increased excitability and concurrently reduced inhibition. Moreover, we showed that this pattern changes already in the earliest stage of semantic processing, when action meanings were negated. Negation modifies the neural representation of the argument in its scope, as soon as semantic effects are observed in the brain.

scholarly journals Affective Priming Effects of Musical Sounds on the Processing of Word Meaning

2011 ◽  
Vol 23 (3) ◽  
pp. 604-621 ◽  
Author(s):  
Nikolaus Steinbeis ◽  
Stefan Koelsch
Keyword(s):  
Emotional Expression ◽  
Word Processing ◽  
Semantic Processing ◽  
Word Meaning ◽  
Behavioral Effect ◽  
Affective Priming ◽  
Emotional Words ◽  
Semantic Level ◽  
Musical Sounds

Recent studies have shown that music is capable of conveying semantically meaningful concepts. Several questions have subsequently arisen particularly with regard to the precise mechanisms underlying the communication of musical meaning as well as the role of specific musical features. The present article reports three studies investigating the role of affect expressed by various musical features in priming subsequent word processing at the semantic level. By means of an affective priming paradigm, it was shown that both musically trained and untrained participants evaluated emotional words congruous to the affect expressed by a preceding chord faster than words incongruous to the preceding chord. This behavioral effect was accompanied by an N400, an ERP typically linked with semantic processing, which was specifically modulated by the (mis)match between the prime and the target. This finding was shown for the musical parameter of consonance/dissonance (Experiment 1) and then extended to mode (major/minor) (Experiment 2) and timbre (Experiment 3). Seeing that the N400 is taken to reflect the processing of meaning, the present findings suggest that the emotional expression of single musical features is understood by listeners as such and is probably processed on a level akin to other affective communications (i.e., prosody or vocalizations) because it interferes with subsequent semantic processing. There were no group differences, suggesting that musical expertise does not have an influence on the processing of emotional expression in music and its semantic connotations.

scholarly journals Miswiring the brain: Human prenatal Δ9-tetrahydrocannabinol use associated with altered fetal hippocampal brain network connectivity

2021 ◽  
pp. 101000
Author(s):  
Moriah E. Thomason ◽  
Ava C. Palopoli ◽  
Nicki N. Jariwala ◽  
Denise M. Werchan ◽  
Alan Chen ◽  
...  
Keyword(s):  
Network Connectivity ◽  
Brain Network ◽  
Brain Network Connectivity ◽  
The Brain

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

Brain reactivity to humorous films is affected by insomnia

SLEEP ◽  
2021 ◽  
Author(s):  
Ernesto Sanz-Arigita ◽  
Yannick Daviaux ◽  
Marc Joliot ◽  
Bixente Dilharreguy ◽  
Jean-Arthur Micoulaud-Franchi ◽  
...  
Keyword(s):  
Emotional Reactivity ◽  
Brain Activity ◽  
Brain Network ◽  
Anterior Cingulate ◽  
Neural Representation ◽  
Cingulate Gyrus ◽  
Anterior Cingulate Gyrus ◽  
Left Caudate ◽  
Neural Reactivity ◽  
The Right

Abstract Study objectives Emotional reactivity to negative stimuli has been investigated in insomnia, but little is known about emotional reactivity to positive stimuli and its neural representation. Methods We used 3T fMRI to determine neural reactivity during the presentation of standardized short, 10-40-s, humorous films in insomnia patients (n=20, 18 females, aged 27.7 +/- 8.6 years) and age-matched individuals without insomnia (n=20, 19 females, aged 26.7 +/- 7.0 years), and assessed humour ratings through a visual analogue scale (VAS). Seed-based functional connectivity was analysed for left and right amygdala networks: group-level mixed-effects analysis (FLAME; FSL) was used to compare amygdala connectivity maps between groups. Results fMRI seed-based analysis of the amygdala revealed stronger neural reactivity in insomnia patients than in controls in several brain network clusters within the reward brain network, without humour rating differences between groups (p = 0.6). For left amygdala connectivity, cluster maxima were in the left caudate (Z=3.88), left putamen (Z=3.79) and left anterior cingulate gyrus (Z=4.11), while for right amygdala connectivity, cluster maxima were in the left caudate (Z=4.05), right insula (Z=3.83) and left anterior cingulate gyrus (Z=4.29). Cluster maxima of the right amygdala network were correlated with hyperarousal scores in insomnia patients only. Conclusions Presentation of humorous films leads to increased brain activity in the neural reward network for insomnia patients compared to controls, related to hyperarousal features in insomnia patients, in the absence of humor rating group differences. These novel findings may benefit insomnia treatment interventions.

scholarly journals The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Mastrandrea ◽  
Fabrizio Piras ◽  
Andrea Gabrielli ◽  
Nerisa Banaj ◽  
Guido Caldarelli ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Network ◽  
Modular Organization ◽  
Node Degree ◽  
Imaging Data ◽  
Brain Disorder ◽  
Functional Connections ◽  
The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

scholarly journals Energy constraints on brain network formation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kosuke Takagi
Keyword(s):  
Network Formation ◽  
Cost Minimization ◽  
Brain Network ◽  
Network Activity ◽  
Rich Functionality ◽  
Energy Constraints ◽  
Dependent Form ◽  
Statistical Similarity ◽  
Simple Ratio ◽  
The Brain

AbstractEnergy constraints are a fundamental limitation of the brain, which is physically embedded in a restricted space. The collective dynamics of neurons through connections enable the brain to achieve rich functionality, but building connections and maintaining activity come at a high cost. The effects of reducing these costs can be found in the characteristic structures of the brain network. Nevertheless, the mechanism by which energy constraints affect the organization and formation of the neuronal network in the brain is unclear. Here, it is shown that a simple model based on cost minimization can reproduce structures characteristic of the brain network. With reference to the behavior of neurons in real brains, the cost function was introduced in an activity-dependent form correlating the activity cost and the wiring cost as a simple ratio. Cost reduction of this ratio resulted in strengthening connections, especially at highly activated nodes, and induced the formation of large clusters. Regarding these network features, statistical similarity was confirmed by comparison to connectome datasets from various real brains. The findings indicate that these networks share an efficient structure maintained with low costs, both for activity and for wiring. These results imply the crucial role of energy constraints in regulating the network activity and structure of the brain.

Sign in / Sign up

Export Citation Format

Share Document