scholarly journals Oscillatory and aperiodic neural activity jointly predict grammar learning

2020 ◽  
Author(s):  
Zachariah R. Cross ◽  
Andrew W. Corcoran ◽  
Matthias Schlesewsky ◽  
Mark. J. Kohler ◽  
Ina Bornkessel-Schlesewsky
Keyword(s):  
Language Processing ◽  
Power Law ◽  
Word Order ◽  
Spectral Power ◽  
Rule Learning ◽  
Oscillatory Activity ◽  
Natural Scenes ◽  
Grammar Learning ◽  
Power Law Exponent ◽  
Behavioural Performance

ABSTRACTMemory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low frequency oscillatory activity underlies successful memory encoding and retrieval. To date, this relationship has predominantly been investigated using objects (e.g., faces, natural scenes); however, considerably less is known about the oscillatory correlates of complex rule learning (e.g., language acquisition). Further, recent work has shown that aperiodic (non-oscillatory) 1/f activity is functionally and behaviourally relevant, yet its interaction with oscillatory activity during complex rule learning remains virtually unknown. Using spectral decomposition and power-law exponent estimation of human EEG data, we show for the first time that 1/f and oscillatory activity jointly influence the learning of different word order rules of a miniature language system. Fixed word order rules were associated with an increased power-law exponent (i.e. steeper 1/f slope) compared to flexible word order rules. We also show that stronger anterior beta synchronisation predicts fixed word order rule learning and subsequent behavioural performance, while stronger theta/alpha synchronisation is associated with the learning of flexible word order rules. These results also revealed nonlinear differences between word order rules as a function of time and sensor space. Moreover, we demonstrated that inter-individual variations in spectral power across the learning task interacted with aperiodic activity to influence subsequent behavioural performance. Together, these results suggest that 1/f activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word order permutations, which manifest in distinct oscillatory profiles.

Thermosonde Measurement of Temperature Structure Parameter and Temperature Spectral Power-Law Exponent Profile in the Lower Stratosphere

2014 ◽  
Vol 34 (5) ◽  
pp. 0501001
Author(s):  
吴晓庆 Wu Xiaoqing ◽  
黄宏华 Huang Honghua ◽  
钱仙妹 Qian Xianmei ◽  
汪平 Wang Ping ◽  
崔朝龙 Cui Chaolong
Keyword(s):  
Power Law ◽  
Lower Stratosphere ◽  
Spectral Power ◽  
Temperature Structure ◽  
Structure Parameter ◽  
Temperature Structure Parameter ◽  
Power Law Exponent

scholarly journals 1/fNeural Noise and Electrophysiological Indices of Contextual Prediction in Normative Aging

2017 ◽  
Author(s):  
S. Dave ◽  
T.A. Brothers ◽  
T.Y. Swaab
Keyword(s):  
Language Processing ◽  
Spectral Power ◽  
Neuronal Firing ◽  
Predictive Processing ◽  
Oscillatory Activity ◽  
Neural Activation ◽  
Discourse Comprehension ◽  
Cross Sectional ◽  
Neural Noise ◽  
Age Related

AbstractPrediction during language comprehension has increasingly been suggested to play a substantive role in efficient language processing. Emerging models have postulated that predictive mechanisms are enhanced when neural networks fire synchronously, but to date, this relationship has been investigated primarily through oscillatory activity in narrow frequency bands. A recently-developed measure proposed to reflect broadband neural activity – and thereby synchronous neuronal firing – is 1/fneural noise extracted from EEG spectral power. Previous research (Voytek et al., 2015) has indicated that this measure of 1/fneural noise changes across the lifespan, and these neural changes predict age-related behavioral impairments in visual working memory. Using a cross-sectional sample of young and older adults, we examined age-related changes in 1/fneural noise and whether this measure would predict ERP correlates of successful lexical prediction during discourse comprehension. 1/fneural noise across two different language tasks revealed high within-subject correlations, indicating that this measure can provide a reliable index of individualized patterns of neural activation. In addition to age, 1/fnoise was a significant predictor of N400 effects of successful lexical prediction, but noise did not mediate age-related declines in other ERP effects. We discuss broader implications of these findings for theories of predictive processing, as well as potential applications of 1/fnoise across research populations.

EEG correlates of cognitive functions and neuropsychiatric disorders: A review of oscillatory activity and neural synchrony abnormalities

2020 ◽  
Vol 16 ◽  
Author(s):  
Meysam Amidfar ◽  
Yong-Ku Kim
Keyword(s):  
Psychiatric Disorders ◽  
Cognitive Functions ◽  
Spectral Power ◽  
Large Body ◽  
Oscillatory Activity ◽  
Brain Oscillations ◽  
Emotional Symptoms ◽  
Obsessive Compulsive ◽  
Neuropsychiatric Diseases ◽  
Obsessive Compulsive Disorders

Background: A large body of evidence suggested that disruption of neural rhythms and synchronization of brain oscillations are correlated with variety of cognitive and perceptual processes. Cognitive deficits are common features of psychiatric disorders that complicate treatment of the motivational, affective and emotional symptoms. Objective: Electrophysiological correlates of cognitive functions will contribute to understanding of neural circuits controlling cognition, the causes of their perturbation in psychiatric disorders and developing novel targets for treatment of cognitive impairments. Methods: This review includes description of brain oscillations in Alzheimer’s disease, bipolar disorder, attentiondeficit/hyperactivity disorder, major depression, obsessive compulsive disorders, anxiety disorders, schizophrenia and autism. Results: The review clearly shows that the reviewed neuropsychiatric diseases are associated with fundamental changes in both spectral power and coherence of EEG oscillations. Conclusion: In this article we examined nature of brain oscillations, association of brain rhythms with cognitive functions and relationship between EEG oscillations and neuropsychiatric diseases. Accordingly, EEG oscillations can most likely be used as biomarkers in psychiatric disorders.

Processing subject focus across two Spanish varieties

Probus ◽  
2020 ◽  
Vol 32 (1) ◽  
pp. 93-127
Author(s):  
Bradley Hoot ◽  
Tania Leal
Keyword(s):  
Language Processing ◽  
Sentence Processing ◽  
Word Order ◽  
Judgment Task ◽  
Spanish Speakers ◽  
Experimental Results ◽  
Final Position ◽  
Reading Task ◽  
New Information ◽  
Basic Facts

AbstractLinguists have keenly studied the realization of focus – the part of the sentence introducing new information – because it involves the interaction of different linguistic modules. Syntacticians have argued that Spanish uses word order for information-structural purposes, marking focused constituents via rightmost movement. However, recent studies have challenged this claim. To contribute sentence-processing evidence, we conducted a self-paced reading task and a judgment task with Mexican and Catalonian Spanish speakers. We found that movement to final position can signal focus in Spanish, in contrast to the aforementioned work. We contextualize our results within the literature, identifying three basic facts that theories of Spanish focus and theories of language processing should explain, and advance a fourth: that mismatches in information-structural expectations can induce processing delays. Finally, we propose that some differences in the existing experimental results may stem from methodological differences.

scholarly journals An Analysis of the External Validity of EEG Spectral Power in an Uncontrolled Outdoor Environment during Default and Complex Neurocognitive States

2021 ◽  
Vol 11 (3) ◽  
pp. 330
Author(s):  
Dalton J. Edwards ◽  
Logan T. Trujillo
Keyword(s):  
External Validity ◽  
Spectral Power ◽  
Brain Activity ◽  
Outdoor Environment ◽  
Oscillatory Activity ◽  
Brain State ◽  
Beta Band ◽  
Eeg Spectral Power ◽  
Eeg Power ◽  
Human Participants

Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions (number sense, attention, memory, executive function) and the other engaging two default brain states. We computed EEG spectral power over three frequency bands (theta: 4–7 Hz, alpha: 8–13 Hz, low beta: 14–20 Hz) where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments.

scholarly journals Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Siyuan Zhao ◽  
Zhiwei Xu ◽  
Limin Liu ◽  
Mengjie Guo ◽  
Jing Yun
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Word Order ◽  
Text Analysis ◽  
Important Application ◽  
Detection Mechanism ◽  
Short Text

Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains. The deceptive opinion detection is an important application of the existing CNN models. The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions. Online opinions are quite short, varying in their types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.

Regional variation of recession flow power-law exponent

2018 ◽  
Vol 32 (7) ◽  
pp. 866-872 ◽  
Author(s):  
Swagat Patnaik ◽  
Basudev Biswal ◽  
Dasika Nagesh Kumar ◽  
Bellie Sivakumar
Keyword(s):  
Power Law ◽  
Regional Variation ◽  
Power Law Exponent ◽  
Flow Power

scholarly journals Growth Rate Exponents of Richtmyer-Meshkov Mixing Layers

2005 ◽  
Vol 73 (3) ◽  
pp. 461-468 ◽  
Author(s):  
Timothy T. Clark ◽  
Ye Zhou
Keyword(s):  
Flow Field ◽  
Power Law ◽  
Mixing Layer ◽  
Power Laws ◽  
Mixing Layers ◽  
Spatial Gradients ◽  
Power Law Exponent ◽  
Virtual Time ◽  
Time Origin ◽  
Density Ratios

The Richtmyer-Meshkov mixing layer is initiated by the passing of a shock over an interface between fluid of differing densities. The energy deposited during the shock passage undergoes a relaxation process during which the fluctuational energy in the flow field decays and the spatial gradients of the flow field decrease in time. This late stage of Richtmyer-Meshkov mixing layers is studied from the viewpoint of self-similarity. Analogies with weakly anisotropic turbulence suggest that both the bubble-side and spike-side widths of the mixing layer should evolve as power-laws in time, with the same power-law exponent and virtual time origin for both sides. The analogy also bounds the power-law exponent between 2∕7 and 1∕2. It is then shown that the assumption of identical power-law exponents for bubbles and spikes yields fits that are in good agreement with experiment at modest density ratios.

scholarly journals Free Convective MHD Flow Past a Vertical Cone with Variable Heat and Mass Flux

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
J. Prakash ◽  
S. Gouse Mohiddin ◽  
S. Vijaya Kumar Varma
Keyword(s):  
Boundary Layer ◽  
Power Law ◽  
Mass Flux ◽  
Numerical Study ◽  
Convection Boundary Layer ◽  
Vertical Cone ◽  
Local Skin ◽  
Surface Mass ◽  
Flow Past ◽  
Power Law Exponent

A numerical study of buoyancy-driven unsteady natural convection boundary layer flow past a vertical cone embedded in a non-Darcian isotropic porous regime with transverse magnetic field applied normal to the surface is considered. The heat and mass flux at the surface of the cone is modeled as a power law according to qwx=xm and qw*(x)=xm, respectively, where x denotes the coordinate along the slant face of the cone. Both Darcian drag and Forchheimer quadratic porous impedance are incorporated into the two-dimensional viscous flow model. The transient boundary layer equations are then nondimensionalized and solved by the Crank-Nicolson implicit difference method. The velocity, temperature, and concentration fields have been studied for the effect of Grashof number, Darcy number, Forchheimer number, Prandtl number, surface heat flux power-law exponent (m), surface mass flux power-law exponent (n), Schmidt number, buoyancy ratio parameter, and semivertical angle of the cone. Present results for selected variables for the purely fluid regime are compared with the published results and are found to be in excellent agreement. The local skin friction, Nusselt number, and Sherwood number are also analyzed graphically. The study finds important applications in geophysical heat transfer, industrial manufacturing processes, and hybrid solar energy systems.

scholarly journals Breakdown coefficients and scaling properties of rain fields

1998 ◽  
Vol 5 (2) ◽  
pp. 93-104 ◽  
Author(s):  
D. Harris ◽  
M. Menabde ◽  
A. Seed ◽  
G. Austin
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Power Law ◽  
Scale Parameter ◽  
Probability Distributions ◽  
Scaling Analysis ◽  
Scaling Properties ◽  
Power Law Exponent ◽  
Scale Similarity ◽  
Parameter Estimation Techniques

Abstract. The theory of scale similarity and breakdown coefficients is applied here to intermittent rainfall data consisting of time series and spatial rain fields. The probability distributions (pdf) of the logarithm of the breakdown coefficients are the principal descriptor used. Rain fields are distinguished as being either multiscaling or multiaffine depending on whether the pdfs of breakdown coefficients are scale similar or scale dependent, respectively. Parameter  estimation techniques are developed which are applicable to both multiscaling and multiaffine fields. The scale parameter (width), σ, of the pdfs of the log-breakdown coefficients is a measure of the intermittency of a field. For multiaffine fields, this scale parameter is found to increase with scale in a power-law fashion consistent with a bounded-cascade picture of rainfall modelling. The resulting power-law exponent, H, is indicative of the smoothness of the field. Some details of breakdown coefficient analysis are addressed and a theoretical link between this analysis and moment scaling analysis is also presented. Breakdown coefficient properties of cascades are also investigated in the context of parameter estimation for modelling purposes.

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