scholarly journals Neural encoding of phrases and sentences in spoken language comprehension

2021 ◽  
Author(s):  
Fan Bai ◽  
Antje S. Meyer ◽  
Andrea E. Martin
Keyword(s):  
Phase Synchronization ◽  
Syntactic Structure ◽  
Natural World ◽  
Neural Response ◽  
Low Frequencies ◽  
Linguistic Structure ◽  
Function Modelling ◽  
Phase Amplitude ◽  
Task Conditions ◽  
The Brain

Speech stands out in the natural world as a biological signal that communicates formally-specifiable complex meanings. However, the acoustic and physical dynamics of speech do not injectively mark the linguistic structure and meaning that we perceive. Linguistic structure must therefore be inferred through the human brain’s endogenous mechanisms, which remain poorly understood. Using electroencephalography, we investigated the neural response to synthesized spoken phrases and sentences that were closely physically-matched but differed in syntactic structure, under either linguistic or non-linguistic task conditions. Differences in syntactic structure were well-captured in theta band (~ 2 to 7 Hz) phase coherence, phase connectivity degree at low frequencies (< ~ 2 Hz), and in both intensity and degree of power connectivity of induced neural response in the alpha band (~ 7.5 to 13.5 Hz). Theta-gamma phase-amplitude coupling was found when participants listened to speech, but it did not discriminate between syntactic structures. Spectral-temporal response function modelling suggested different encoding states in both temporal and spectral dimensions as a function of the amount and type of linguistic structure perceived, over and above the acoustically-driven neural response. Our findings provide a comprehensive description of how the brain separates linguistic structures in the dynamics of neural responses, and imply that phase synchronization and strength of connectivity can be used as readouts for constituent structure, providing a novel basis for future neurophysiological research on linguistic structure in the brain.

scholarly journals Phase synchronization varies systematically with linguistic structure composition

2019 ◽  
Vol 375 (1791) ◽  
pp. 20190305 ◽  
Author(s):  
Jonathan R. Brennan ◽  
Andrea E. Martin
Keyword(s):  
Language Processing ◽  
Phase Synchronization ◽  
Natural Extension ◽  
Function Words ◽  
Linguistic Structure ◽  
Link Type ◽  
Phase Alignment ◽  
The Time Domain ◽  
Linguistic Structures ◽  
The Brain

Computation in neuronal assemblies is putatively reflected in the excitatory and inhibitory cycles of activation distributed throughout the brain. In speech and language processing, coordination of these cycles resulting in phase synchronization has been argued to reflect the integration of information on different timescales (e.g. segmenting acoustics signals to phonemic and syllabic representations; (Giraud and Poeppel 2012 Nat. Neurosci. 15 , 511 ( doi:10.1038/nn.3063 )). A natural extension of this claim is that phase synchronization functions similarly to support the inference of more abstract higher-level linguistic structures (Martin 2016 Front. Psychol. 7 , 120; Martin and Doumas 2017 PLoS Biol . 15 , e2000663 ( doi:10.1371/journal.pbio.2000663 ); Martin and Doumas. 2019 Curr. Opin. Behav. Sci. 29 , 77–83 ( doi:10.1016/j.cobeha.2019.04.008 )). Hale et al . (Hale et al . 2018 Finding syntax in human encephalography with beam search. arXiv 1806.04127 ( http://arxiv.org/abs/1806.04127 )) showed that syntactically driven parsing decisions predict electroencephalography (EEG) responses in the time domain; here we ask whether phase synchronization in the form of either inter-trial phrase coherence or cross-frequency coupling (CFC) between high-frequency (i.e. gamma) bursts and lower-frequency carrier signals (i.e. delta, theta), changes as the linguistic structures of compositional meaning ( viz ., bracket completions, as denoted by the onset of words that complete phrases) accrue. We use a naturalistic story-listening EEG dataset from Hale et al . to assess the relationship between linguistic structure and phase alignment. We observe increased phase synchronization as a function of phrase counts in the delta, theta, and gamma bands, especially for function words. A more complex pattern emerged for CFC as phrase count changed, possibly related to the lack of a one-to-one mapping between ‘size’ of linguistic structure and frequency band—an assumption that is tacit in recent frameworks. These results emphasize the important role that phase synchronization, desynchronization, and thus, inhibition, play in the construction of compositional meaning by distributed neural networks in the brain. This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.

Tuning the brain for novelty detection under emotional threat: the role of specific gamma-band phase synchronization

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S42
Author(s):  
M Garcia-Garcia ◽  
J Yordanova ◽  
V Kolev ◽  
J Dominguez-Borras ◽  
C Escera
Keyword(s):  
Phase Synchronization ◽  
Novelty Detection ◽  
Gamma Band ◽  
The Brain

scholarly journals Task dependence of neural representations of global scene properties but not scene categories in the prefrontal cortex

2020 ◽  
Author(s):  
Yaelan Jung ◽  
Dirk B. Walther
Keyword(s):  
Prefrontal Cortex ◽  
Visual Cortex ◽  
Scene Perception ◽  
Sound Level ◽  
Task Context ◽  
Complex Scene ◽  
Neural Representations ◽  
Task Conditions ◽  
Scene Content ◽  
The Brain

AbstractNatural scenes deliver rich sensory information about the world. Decades of research has shown that the scene-selective network in the visual cortex represents various aspects of scenes. It is, however, unknown how such complex scene information is processed beyond the visual cortex, such as in the prefrontal cortex. It is also unknown how task context impacts the process of scene perception, modulating which scene content is represented in the brain. In this study, we investigate these questions using scene images from four natural scene categories, which also depict two types of global scene properties, temperature (warm or cold), and sound-level (noisy or quiet). A group of healthy human subjects from both sexes participated in the present study using fMRI. In the study, participants viewed scene images under two different task conditions; temperature judgment and sound-level judgment. We analyzed how different scene attributes (scene categories, temperature, and sound-level information) are represented across the brain under these task conditions. Our findings show that global scene properties are only represented in the brain, especially in the prefrontal cortex, when they are task-relevant. However, scene categories are represented in the brain, in both the parahippocampal place area and the prefrontal cortex, regardless of task context. These findings suggest that the prefrontal cortex selectively represents scene content according to task demands, but this task selectivity depends on the types of scene content; task modulates neural representations of global scene properties but not of scene categories.

scholarly journals Neurophysiological dynamics of phrase-structure building during sentence processing

2017 ◽  
Vol 114 (18) ◽  
pp. E3669-E3678 ◽  
Author(s):  
Matthew J. Nelson ◽  
Imen El Karoui ◽  
Kristof Giber ◽  
Xiaofang Yang ◽  
Laurent Cohen ◽  
...  
Keyword(s):  
Sentence Processing ◽  
Model Comparison ◽  
Formal Model ◽  
Syntactic Structure ◽  
Brain Activity ◽  
Transition Probability ◽  
Middle Temporal Gyrus ◽  
Similar Amount ◽  
Gamma Power ◽  
The Brain

Although sentences unfold sequentially, one word at a time, most linguistic theories propose that their underlying syntactic structure involves a tree of nested phrases rather than a linear sequence of words. Whether and how the brain builds such structures, however, remains largely unknown. Here, we used human intracranial recordings and visual word-by-word presentation of sentences and word lists to investigate how left-hemispheric brain activity varies during the formation of phrase structures. In a broad set of language-related areas, comprising multiple superior temporal and inferior frontal sites, high-gamma power increased with each successive word in a sentence but decreased suddenly whenever words could be merged into a phrase. Regression analyses showed that each additional word or multiword phrase contributed a similar amount of additional brain activity, providing evidence for a merge operation that applies equally to linguistic objects of arbitrary complexity. More superficial models of language, based solely on sequential transition probability over lexical and syntactic categories, only captured activity in the posterior middle temporal gyrus. Formal model comparison indicated that the model of multiword phrase construction provided a better fit than probability-based models at most sites in superior temporal and inferior frontal cortices. Activity in those regions was consistent with a neural implementation of a bottom-up or left-corner parser of the incoming language stream. Our results provide initial intracranial evidence for the neurophysiological reality of the merge operation postulated by linguists and suggest that the brain compresses syntactically well-formed sequences of words into a hierarchy of nested phrases.

Hippocampal-prefrontal theta coupling develops as mice become proficient in associative odorant discrimination learning

2021 ◽  
Author(s):  
Daniel Ramirez-Gordillo ◽  
Andrew A. Parra ◽  
K. Ulrich Bayer ◽  
Diego Restrepo
Keyword(s):  
Learning And Memory ◽  
Discrimination Learning ◽  
Gamma Oscillations ◽  
Oscillatory Activity ◽  
Dependent Protein Kinase ◽  
Dependent Protein ◽  
Theta Phase ◽  
Phase Amplitude ◽  
The Brain

Learning and memory requires coordinated activity between different regions of the brain. Here we studied the interaction between medial prefrontal cortex (mPFC) and hippocampal dorsal CA1 during associative odorant discrimination learning in the mouse. We found that as the animal learns to discriminate odorants in a go-no go task the coupling of high frequency neural oscillations to the phase of theta oscillations (phase-amplitude coupling or PAC) changes in a manner that results in divergence between rewarded and unrewarded odorant-elicited changes in the theta-phase referenced power (tPRP) for beta and gamma oscillations. In addition, in the proficient animal there was a decrease in the coordinated oscillatory activity between CA1 and mPFC in the presence of the unrewarded odorant. Furthermore, the changes in PAC resulted in a marked increase in the accuracy for decoding odorant identity from tPRP when the animal became proficient. Finally, we studied the role of Ca2+/calmodulin-dependent protein kinase II α (CaMKIIα), a protein involved in learning and memory, in oscillatory neural processing in this task. We find that the accuracy for decoding the odorant identity from tPRP decreases in CaMKIIα knockout mice and that this accuracy correlates with behavioral performance. These results implicate a role for PAC and CaMKIIα in olfactory go-no go associative learning in the hippocampal-prefrontal circuit.

Special Issue: Evolutionary Design

1999 ◽  
Vol 13 (3) ◽  
pp. 143-143
Author(s):  
PETER BENTLEY
Keyword(s):  
Immune System ◽  
Design Process ◽  
Small Variation ◽  
Natural World ◽  
Evolutionary Design ◽  
Next Generation ◽  
Special Issue ◽  
Natural Evolution ◽  
Human Endeavor ◽  
The Brain

Throughout the natural world and our human-designed world, design and evolution seem to go hand-in-hand. Some of the most astonishing and complex designs known to humankind—the embryogeny process, the immune system, the brain, the very structure of DNA—are products of natural evolution, not human endeavor. In addition, the progress of our own designs seems evolutionary, as the best concepts from existing designs are combined with some small variation to produce the next generation of cars, computers, and indeed, most types of human design. And now, as the papers in this and subsequent special issues will show, our computers are allowing us to harness the power of evolution directly, to aid the design process.

scholarly journals The Neural Correlates of Time: A Meta-analysis of Neuroimaging Studies

2019 ◽  
Vol 31 (12) ◽  
pp. 1796-1826 ◽  
Author(s):  
Andrea Nani ◽  
Jordi Manuello ◽  
Donato Liloia ◽  
Sergio Duca ◽  
Tommaso Costa ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Analytical Investigation ◽  
Neural Systems ◽  
Subcortical Structures ◽  
Time Intervals ◽  
Sense Of Time ◽  
Task Conditions ◽  
Estimation Of Time ◽  
The Brain ◽  
Neuroimaging Techniques

During the last two decades, our inner sense of time has been repeatedly studied with the help of neuroimaging techniques. These investigations have suggested the specific involvement of different brain areas in temporal processing. At least two distinct neural systems are likely to play a role in measuring time: One is mainly constituted of subcortical structures and is supposed to be more related to the estimation of time intervals below the 1-sec range (subsecond timing tasks), and the other is mainly constituted of cortical areas and is supposed to be more related to the estimation of time intervals above the 1-sec range (suprasecond timing tasks). Tasks can then be performed in motor or nonmotor (perceptual) conditions, thus providing four different categories of time processing. Our meta-analytical investigation partly confirms the findings of previous meta-analytical works. Both sub- and suprasecond tasks recruit cortical and subcortical areas, but subcortical areas are more intensely activated in subsecond tasks than in suprasecond tasks, which instead receive more contributions from cortical activations. All the conditions, however, show strong activations in the SMA, whose rostral and caudal parts have an important role not only in the discrimination of different time intervals but also in relation to the nature of the task conditions. This area, along with the striatum (especially the putamen) and the claustrum, is supposed to be an essential node in the different networks engaged when the brain creates our sense of time.

scholarly journals Seasonal synchronization of foodborne outbreaks in the United States, 1996–2017

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ryan B. Simpson ◽  
Bingjie Zhou ◽  
Elena N. Naumova
Keyword(s):  
Food Systems ◽  
Negative Binomial ◽  
Phase Synchronization ◽  
Dynamic Networks ◽  
The United States ◽  
Foodborne Outbreaks ◽  
Seasonal Synchronization ◽  
Phase Amplitude ◽  
And Control ◽  
Phase Phase

Abstract Modern food systems represent complex dynamic networks vulnerable to foodborne infectious outbreaks difficult to track and control. Seasonal co-occurrences (alignment of seasonal peaks) and synchronization (similarity of seasonal patterns) of infections are noted, yet rarely explored due to their complexity and methodological limitations. We proposed a systematic approach to evaluate the co-occurrence of seasonal peaks using a combination of L-moments, seasonality characteristics such as the timing (phase) and intensity (amplitude) of peaks, and three metrics of serial, phase-phase, and phase-amplitude synchronization. We used public records on counts of nine foodborne infections abstracted from CDC’s FoodNet Fast online platform for the US and ten representative states from 1996 to 2017 (264 months). Based on annualized and trend-adjusted Negative Binomial Harmonic Regression (NBHR) models augmented with the δ-method, we determined that seasonal peaks of Campylobacter, Salmonella, and Shiga toxin-producing Escherichia Coli (STEC) were tightly clustered in late-July at the national and state levels. Phase-phase synchronization was observed between Cryptosporidium and Shigella, Listeria, and Salmonella (ρ = 0.51, 0.51, 0.46; p < 0.04). Later peak timing of STEC was associated with greater amplitude nationally (ρ = 0.50, p = 0.02) indicating phase-amplitude synchronization. Understanding of disease seasonal synchronization is essential for developing reliable outbreak forecasts and informing stakeholders on mitigation and preventive measures.

EEG Based BCI Using Visual Imagery Task for Robot Control

2013 ◽  
Vol 61 (2) ◽  
Author(s):  
Husnaini Azmy ◽  
Norlaili Mat Safri
Keyword(s):  
Spectral Power ◽  
Normal Subjects ◽  
Imagery Task ◽  
Non Invasive ◽  
Cortex Area ◽  
Wireless Robot ◽  
Scalp Location ◽  
Task Conditions ◽  
Electroencephalogram Eeg ◽  
The Brain

The aim of this study is to detect the brain activation on scalp by Electroencephalogram (EEG) task–based for brain computer interface (BCI) using wirelessly control robot. EEG was measured in 8 normal subjects for control and task conditions. The objective is to determine one scalp location which will give signals that can be used to control the wireless robot using BCI and EEG, using non invasive and without subject training. In control condition subjects were ask to relax but in task condition, subjects were asked to imagine a star rotating clockwise at position 45 degrees direction pointed by the wireless robot where at this angle the target is located. At position 0 and 90 degree angle subjects were asked to relax since there is no target on that direction. Using EEG spectral power analysis and normalization, the optimum location for this task has been detected at position F8 which is in frontal cortex area and the rhythm happened at alpha frequency band. At this position, the signals from the brain should be able to drive the robot to the required direction by giving correct and accurate signals to robot moving towards target.

scholarly journals Candy and the brain: neural response to candy gains and losses

2013 ◽  
Vol 13 (3) ◽  
pp. 437-451 ◽  
Author(s):  
Katherine R. Luking ◽  
Deanna M. Barch
Keyword(s):  
Neural Response ◽  
Gains And Losses ◽  
The Brain
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