scholarly journals Psychophysiological Mechanisms of Grammatical Structuring in the Speech Activity of a Preschool Child

2021 ◽  
Vol 12 (3) ◽  
pp. 255-269
Author(s):  
Kateryna Kruty ◽  
Tetiana Bohdan ◽  
Marharyta Kozyr ◽  
Oleksandra Sviontyk ◽  
Tetiana Shvaliuk ◽  
...  

Language as a means of implementing the speech process is an independent system with its own structure. In the context of our research, the concept of M.I. Zhynkin (1958) on the grid distribution of information in the grammatical space, which explains the mechanism of perception and awareness of speech. It is important for us to conclude that the sooner a direct connection is formed between the conceptual system and the basal ganglia, the better the child's awareness, assimilation and use of grammatical categories. To organize the normal functioning of speech requires a complex coordinated work of millions of neural elements of the brain, which are included in its various parts. It is proved that after ten years the ability to develop neural networks necessary for the construction of speech centers it disappears. The problem of forming grammatically correct speech in preschool children can be solved quickly and efficiently if you intensify the interaction of different analyzers. It is proved that the sensory information complex consists of auditory, visual and tactile images, which, complementing, amplifying each other, increase the number of useful signals, expand the speech space, which, in turn, limits the choice of adequate speech pattern during acquisition, perception and oral awareness. Children's learning of the elements of the grammatical system of language is influenced by two main factors, namely: the dependence on the simplicity or complexity of the language phenomenon and the degree of its communicative significance. The formation of grammatically correct speech (morphology, word formation, syntax) is based on a certain cognitive development of the child.

2010 ◽  
Author(s):  
Αικατερίνη Χαραλαμποπούλου

In this study I have attempted to present a linguistic investigation into the nature and structure of time, based on proposals developed in Evans (2004). Accordingly, as linguistic structure and particularly patterns of elaboration reflect conceptual structure conventionalized into a format encodable in language, this study presents an examination of the human conceptual system for time. Indeed, an examination of the ways in which language lexicalizes time provides important insights into the nature and organization of time. That is, given the widely held assumption that semantic structure derives from and reflects, at least partially, conceptual structure, language offers a direct way of investigating the human conceptual system. However, how time is realized at the conceptual level, that is, how we represent time as revealed by the way temporal concepts are encoded in language, does not tell the whole story, if we are to uncover the nature and structure of time. Research in cognitive science suggests that phenomenological experience and the nature of the external world of sensory experience to which subjective experience constitutes a response, give rise to our pre- conceptual experience of time. In other words, as Evans (2004) says, time is not restricted to one particular layer of experience but it rather “constitutes a complex range of phenomena and processes which relate to different levels and kinds of experience” (ibid.: 5). Accordingly, while my focus in this study is on the temporal structure, which is to say the organization and structuring of temporal concepts, at the conceptual level, I have also attempted to present an examination of the nature of temporal experience at the pre-conceptual level (prior to representation in conceptual structure). In this regard, I have examined the results of research from neuroscience, cognitive psychology and social psychology. More specifically and with respect to evidence from neuroscience, it is suggested that temporal experience is ultimately grounded in neurological mechanisms necessary for regulating and facilitating perception (e.g., Pöppel 1994). That is, perceptual processing is underpinned by the occurrence of neurologically instantiated temporal intervals, the perceptual moments, which facilitate the integration of sensory information into coherent percepts. As we have seen, there is no single place in the brain where perceptual input derived from different modalities, or even information from within the same modality, can be integrated. In other words, there is no one place where spatially distributed sensory information associated with the distinct perceptual processing areas of the brain, are integrated in order to produce a coherent percept. Rather, what seems to be the case is that the integration of sensory information into coherent percepts is enabled by the phenomena of periodic perceptual moments. Such a mechanism enables us to perceive, in that the nature of our percepts are in an important sense ‘constructed’. Put another way, perception is a kind of constructive process which updates successive perceptual information to which an organism has access. The updating occurs by virtue of innate timing mechanisms, the perceptual moments, which occur at all levels of neurological processing and range from a fraction of second up to an outer limit of about three seconds. It is these timing mechanisms which form the basis of our temporal experience. As Gell says, “perception is intrinsically time-perception, and conversely, time-perception, or internal time-consciousness, is just perception itself...That is to say, time is not something we encounter as a feature of contingent reality, as if it lay outside us, waiting to be perceived along with tables and chairs and the rest of the perceptible contents of the universe. Instead, subjective time arises as inescapable feature of the perceptual process itself, which enters into the perception of anything whatsoever” (1992: 231). In other words, our experience of time is a consequence of the various innate ‘timing mechanisms' in the brain which give rise to a range of perceptual moments, which are in turn necessary for and underpin perceptual processing. In this way, time exists into the experience of everything as it is fundamental to the way in which perceptual process operates. […]


2007 ◽  
Vol 362 (1479) ◽  
pp. 473-481 ◽  
Author(s):  
Roddy Williamson ◽  
Abdul Chrachri

Artificial neural networks (ANNs) have become increasingly sophisticated and are widely used for the extraction of patterns or meaning from complicated or imprecise datasets. At the same time, our knowledge of the biological systems that inspired these ANNs has also progressed and a range of model systems are emerging where there is detailed information not only on the architecture and components of the system but also on their ontogeny, plasticity and the adaptive characteristics of their interconnections. We describe here a biological neural network contained in the cephalopod statocysts; the statocysts are analogous to the vertebrae vestibular system and provide the animal with sensory information on its orientation and movements in space. The statocyst network comprises only a small number of cells, made up of just three classes of neurons but, in combination with the large efferent innervation from the brain, forms an ‘active’ sense organs that uses feedback and feed-forward mechanisms to alter and dynamically modulate the activity within cells and how the various components are interconnected. The neurons are fully accessible to physiological investigation and the system provides an excellent model for describing the mechanisms underlying the operation of a sophisticated neural network.


Author(s):  
Georgia Konstantopoulou ◽  

When we talk about psychosis and psychotic disorders, we have in mind patients with disorganized thinking, mental retardation, delusions, and other similar symptoms associated with damage to the brain's normal functioning. Psychosis, however, is not the only cause of dysfunction, the abnormal functioning of the brain. The onset of psychosis may be due to psychological factors, with stress to be one of the main factors. Psychological and environmental factors interact with biological ones creating fertile ground for the development of psychosis. Anxiety, stress, depression, immigration, social stress, and consequently stressful life events are the leading causes of a psychotic episode. In this article, we will try to examine the following parameters: 1) what are the psychological-environmental factors that contribute to the onset of psychosis, and 2) what is their relationship with biological factors.


1999 ◽  
Vol 13 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Laurence Casini ◽  
Françoise Macar ◽  
Marie-Hélène Giard

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.


2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.


Author(s):  
Ann-Sophie Barwich

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.


Author(s):  
Ryan Cotterell ◽  
Hinrich Schütze

Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word questionably can be analyzed as question+ able+ ly. However, this structural decomposition of the word does not directly give us a semantic representation of the word’s meaning. Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts. In this work, we propose a novel probabilistic model of word formation that captures both the analysis of a word w into its constituent segments and the synthesis of the meaning of w from the meanings of those segments. Our model jointly learns to segment words into morphemes and compose distributional semantic vectors of those morphemes. We experiment with the model on English CELEX data and German DErivBase (Zeller et al., 2013) data. We show that jointly modeling semantics increases both segmentation accuracy and morpheme F1 by between 3% and 5%. Additionally, we investigate different models of vector composition, showing that recurrent neural networks yield an improvement over simple additive models. Finally, we study the degree to which the representations correspond to a linguist’s notion of morphological productivity.


1989 ◽  
Vol 1 (3) ◽  
pp. 201-222 ◽  
Author(s):  
Adam N. Mamelak ◽  
J. Allan Hobson

Bizarreness is a cognitive feature common to REM sleep dreams, which can be easily measured. Because bizarreness is highly specific to dreaming, we propose that it is most likely brought about by changes in neuronal activity that are specific to REM sleep. At the level of the dream plot, bizarreness can be defined as either discontinuity or incongruity. In addition, the dreamer's thoughts about the plot may be logically deficient. We propose that dream bizarreness is the cognitive concomitant of two kinds of changes in neuronal dynamics during REM sleep. One is the disinhibition of forebrain networks caused by the withdrawal of the modulatory influences of norepinephrine (NE) and serotonin (5HT) in REM sleep, secondary to cessation of firing of locus coeruleus and dorsal raphe neurons. This aminergic demodulation can be mathematically modeled as a shift toward increased error at the outputs from neural networks, and these errors might be represented cognitively as incongruities and/or discontinuities. We also consider the possibility that discontinuities are the cognitive concomitant of sudden bifurcations or “jumps” in the responses of forebrain neuronal networks. These bifurcations are caused by phasic discharge of pontogeniculooccipital (PGO) neurons during REM sleep, providing a source of cholinergic modulation to the forebrain which could evoke unpredictable network responses. When phasic PGO activity stops, the resultant activity in the brain may be wholly unrelated to patterns of activity dominant before such phasic stimulation began. Mathematically such sudden shifts from one pattern of activity to a second, unrelated one is called a bifurcation. We propose that the neuronal bifurcations brought about by PGO activity might be represented cognitively as bizarre discontinuities of dream plot. We regard these proposals as preliminary attempts to model the relationship between dream cognition and REM sleep neurophysiology. This neurophysiological model of dream bizarreness may also prove useful in understanding the contributions of REM sleep to the developmental and experiential plasticity of the cerebral cortex.


2010 ◽  
Vol 61 (2) ◽  
pp. 120-124 ◽  
Author(s):  
Ladislav Zjavka

Generalization of Patterns by Identification with Polynomial Neural Network Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.


2004 ◽  
Vol 27 (3) ◽  
pp. 377-396 ◽  
Author(s):  
Rick Grush

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.


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