State Of Human Mind Research Articles

Philosophical Analysis of Choreographic Language: The Nature and Specificity of Dance Expressiveness

Productive analysis of the language of choreographic art in order to determine the specifics of dance expressiveness requires the solution of three main tasks. Firstly, to explain the mechanism of functioning of the kinetic language; secondly, to highlight the expressive possibilities of dance, mime and plasticity; thirdly, to understand the ontological foundations of human motor activity in dance. It is not possible to solve the set tasks within the framework of cultural or semiotic approaches only. In this way, the phenomenon of dance is interpreted according to its representation in artistic culture, in the context of its functioning in the process of semiosis, and dance movements are initially endowed with communicative properties. The solution of the set tasks was carried out at the level of philosophical reflection. It was determined that a human being, as a result of the association of ideas and impressions in his consciousness, is peculiar to endow all objects and phenomena of reality perceived by him with meaning. Thus, the association of ideas and impressions in the human consciousness is the functional basis of kinetic language. But absolutely any manifestation of human motor activity can be endowed with meaning in a similar way. That is why dance, pantomime and plasticity — three related expressive means using human body movements as a material — were singled out in the kinetic language. Since it is impossible to understand the nature of dance movements on the basis of their external features, a hypothesis was proposed according to which these features themselves are conditioned by the inner state of mind of the dancer. Dance was defined as a type of human motor activity genetically connected with the “affect of joy” and aimed at maintaining and preserving its action. The conclusion is made: the specificity of dance expressiveness lies in the translation of a unique human state of mind, which is characterized by the feeling of unity of the rise of the body’s vitality and the increased ability of the soul to feel and perceive life in its fullness.

1920년대 미국 사회의 심리상태에 대한 정신분석학적 연구

In the 1920s, American society had been already filled with romantic wonder and riches. However, the Materialism that resulted from rapid industrialization ruined universal hopes and ideal values of the American society including American Dream and American Traditional Innocence and so on.
 The aim of this thesis is to look into and examine the characters’ social backgrounds in Fitzgerald’s The Great Gatsby with regard to psychoanalytical perspectives, which help us to understand whether the atmosphere of American society was matched or not in the 1920s. Thus, as one way to closely approach these issues, we analyze the states of mind within the characters focused on Freudian and Jungian notion of psychoanalysis theories.
 According to Freud’s psychoanalytic theory. there are three steps(=Id-Ego-Super Ego) that play parts in moral judgment and control within the states of human mind. Therefore, when psychoanalytic theory applies to the characters in The Great Gatsby, we can distinguish them as three groups; Firstly, the main character, Gatsby, who didn’t realize ideal values of American society in the 1920s represents the Ego world. Secondly, an observant bastard Tom and his wife Daisy, Myrtle, and Jordan are in the Id state.
 In fact, we are willing to say that Fitzgerald’s The Great Gatsby is his autobiographical work to apprise the corruption of American society at that time he lived. Hence, the reason why Freud’s theory was applied to the characters in The Great Gatsby is to help not only understand many persons’ psychology from the characters of Fitzgerald’s work on American society in the 1920s but also heal those who live in the modern society through the psychoanalytical approach.

RNN LSTM-based emotion recognition using EEG signals

Emotion, a representation of the human state of mind, plays an important role in day-to-day human life and helps one make good decisions. A typical way to understand human emotion is by observing a person's facial expressions and modulation of speech, and it can be categorized as sad, angry, happy, fearful, and so on. Emotion recognition using Brain Computer Interface (BCI) systems is beneficial for patients suffering from paralysis, autism, and mental retardation who cannot express their emotions like regular people. In this paper, after analyzing several data mining algorithms and various Neural Network models such as Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), and the Bi-directional RNN it has been proposed that Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) based emotion recognition using Electroencephalography (EEG) signals provides a better result. The main purpose of this paper is to introduce models which can work better than the existing ones on the K-EmoCon dataset. The metrics used in this paper are valence and arousal. The proposed RNN-LSTM model achieves a valence accuracy of 69.85% and an arousal accuracy of 45.07%. This model improves the accuracy of emotion detection on the K-EmoCon dataset. This approach achieves 4% more accuracy when compared to existing models such as the Convolution-augmented Transformer.

Description of various Ragas in Vedas and their scientific impact on human emotions

There is a deep connection between music and Vedas. Music has been described in the Vedas as an important part of the creation of the universe. Music has been ranked as "Nadabrahma" in the Vedas, which is considered a symbol of meditation and bliss of Brahma. Music has been considered in the Vedas as a medium for the upliftment and progress of the human soul. There are four major Vedas in the Vedas – Rigveda, Yajurveda, Samaveda, and Atharvaveda. Music finds special mention in the Samaveda, which provides inspiration and ideality for kirtan and worship. In the Vedas, music has been considered an important medium to take the energy of the soul to the heights, which promotes musicalization of the soul. Furthermore, music has been associated in the Vedas with time, place, and circumstances, thereby relating it to the society, cultural, and spiritual environment. The description of various ragas in the Vedas refers to the stimulation and stabilization of human emotions. . Emotions like meditation, peace, enthusiasm, love, etc. are stimulated through music in the Vedas, which improves the state of human mind. Music is related to human brain function, emotional balance, and mental health. The use of different ragas and swaras activates certain areas of the brain and produces emotionally based responses. The highest purpose of music in the Vedas is the development of the soul. The combined experience of melodious notes of different ragas establishes a dialogue with the soul and encourages spiritual upliftment. Through the presented research paper, we also want to know what is the scientific impact of music in the Vedas and the form of ragas mentioned in it on human emotions. Will try.

Jurisprudence activity based theory: general characteristics

The article is devoted to general characteristics of the activity based theory as a constituent of jurisprudence, a theory by means of which legal phenomena can be interpreted. It is emphasized that the activity based theory could be methodological means of knowledge of law that has not become the subject of comprehensive knowledge at present. It is noted that psychology has long considered the theory of activity as a constituent part of the epistemology of its subject, but there were not conducted comprehensive research of the activity based theory as a component of jurisprudence within the domestic legal science.
 Attention is paid on three components of the theory of activity allowing us to consider it as a possible constituent of jurisprudence. The object is the first crucial feature of the activity based theory. Legal phenomena as dynamic phenomena are being the object of the activity based theory that in turn is a component of jurisprudence The second important characteristic feature of the activity based theory is the content The activity based theory is a system of knowledge about legal phenomena. This knowledge allows revealing the essence, functioning and legal phenomena’s development tendencies. The third significant characteristic feature of the activity based theory is the conceptual and categorical apparatus. It is noted that the terminology of the theory of activity largely includes terms inherent in everyday language, and such a feature of the theory of activity terminology has not interfered with the possibility of its use in explaining psychological phenomena.
 It is summarized that the further national legal system development aimed at entering the European legal culture involves a change in the paradigm of perception of law, and interpretation of legal phenomena. One of the important means of ensuring the effectiveness of law and overcoming legal dogmatism is the jurisprudence activity based theory. The potential epistemological possibilities of the theory of activity in the field of law are determined by the inherent human nature of the desire for activity, coexistence in law and order, and the connection of law with the human state of mind. The jurisprudence activity based theory can be a means of interpreting of legal phenomena as dynamic phenomena. The social nature of law determines the variability of law that should correspond to the relations governed by legal norms. The jurisprudence activity based theory allows finding out legal phenomena’s origin, functioning and trends in their development.

Human Intelligence and Consciousness

From recognition of the unity (similarity) of all types of the matter motions it follows that at the level of a human it manifests in living activities. Logically, that this basic activity of human state of mind and consciousness development should possess some structure, manifesting it. In this connection proposed by the author intelligence organ of each human being, evolutionary appearing and intravitally developing and operating, starts showing fundamental nature of a human and society in common. Thereby it sets general methodological regularity of people daily living activities.

Blue Economy Policy Model for the Small Island Developing States: The Case of Zanzibar

The sustainable development of world social economic actors, activities and sectors depends upon the nature and character of world’s oceans, seas and coastal areas – the largest ecosystem on the planet. The global blue economy policy objective articulated in form of the Sustainable Development Goal # 14: is to conserve and sustainably use the oceans, seas and marine resources for sustainable social economic development. The paper presents the Blue Economy Policy Model for the Small independent Developing Island States, (SIDS) using an analytical policy model. The Blue Economy Policy Model considers wealth resources as stock and flow variables and theses are dynamic and complex production transformation function of exogenous, endogenous variables, other interdependent operations and social economic welfare. The solution and policy objective are to optimize wealth resources, for sustainable social economic welfare. The social economic welfare is a “happy state of human mind” in form the sum total of all satisfactions experienced by an individual and communities as the sum total of individual welfares in a certain geographical location. The paper recommends that in order to achieve the “transparent, equitable and optimal exploitation of all wealth resources to underpin broad-based sustainable growth and socio-economic development” the small independent developing states such as Zanzibar must take into account conditions and the importance of strengthening all wealth resource-based linkages while resources are extant so as to maximize the developmental and inter-generational impact of those resources. Also, this include the ability to see beyond national corporates and boundaries permits recognition of serious global development markets and trade threats that lie elsewhere within the dynamic context as well as the chance to pursue opportunities that would otherwise be missed.

Subject-Dependent Emotion Recognition System Based on Multidimensional Electroencephalographic Signals: A Riemannian Geometry Approach

Emotion recognition plays an important role in human computer interaction systems as it helps the computer in understanding human behavior and their decision making process. Using Electroencephalographic (EEG) signals in emotion recognition offers a direct assessment on the inner state of human mind. This study aims to build a subject dependent emotion recognition system that differentiate between high and low levels of valance and arousal, using multidimensional EEG signals. Our system offers a transfer learning- minimum distance to Riemannian mean (TL-MDRM) framework. In this work, we perform two pre-processing stages. In the first stage, we analyze the EEG signals to investigate their non-Gaussianity and determine the most appropriate signal distribution. Using several statistical and goodness of fit tests, T-distribution was found to be the most appropriate distribution. Covariance matrix estimations plays a crucial step in manifold learning technique, based on the most suitable signal distribution the covariance matrix estimation technique is chosen. In the second stage, we perform transfer learning to deal with cross-session variability by generating a unique reference point for each participant and performing affine transformation for the covariance matrices on the symmetric positive definite (SPD) manifold around this point. The results show that, TL process improved the performance even when assuming Gaussian distribution, while assuming T-distribution with TL improved the performance further.

A Quantitative Study on Statistical Associations between Extroversion-Introversion Personality Traits and Anxiety in Different Stages of Lockdown during the Covid-19 Pandemic

The Covid-19 pandemic has caused several changes in the human state of mind, in particular adapting to the culture of the new normal while lockdown measures are implemented. This study explored the effect of the lockdown measure on the level of anxiety of high school students, comparing those identified as introverts and extroverts. Participants (N = 103) filled out the given survey, which determined that they were both introverts or extroverts and the level of anxiety that they had before, during, and after the lockdown caused by the pandemic. According to statistical analysis, the result showed that the level of anxiety perceived by those feeling the sense of extroversion was statistically higher than those with introversion, at the significance level of 95%. In addition, the analysis revealed that there was no correlation between extroverts and anxiety before, during, and after the lockdown measures. On the other hand, there were statistical correlations between the level of introversion and the level of anxiety in every stage of lockdown: before, during and after, indicating that the lockdowns due to the global pandemic did not affect extroverted people anxiety as much as it affected introverts. Moreover, it also showed that the level of anxiety of the introverts has become even more intensified even after the lockdown.

Performance Comparison of Different Convolutional Neural Network Approaches for Facial Expression Recognition

Abstract: Facial expression is a non-verbal way of communication to express the human state of mind using facial muscles. Happiness, sadness, anger, surprise, disgust, fear, and neutral expressions are widely used in the field of medical rehabilitation, sentiment analysis, counseling, and so on inspiring researchers to develop effective models to classify the expressions effectively. LeNet5, AlexNet, Deep Model, Shallow Model, Deep CNN Model are some commonly used models that have been developed to recognize facial expressions using machine learning and deep learning. In this research, a new convolutional neural network model has been proposed and compared with the existing models. The FER-2013 dataset has been used to evaluate the performance using different metrics to find the efficiency of the models. The proposed model provides comparatively better accuracy than most of the existing models, which is 64.4%. Keywords: Facial Expression, Image Classification, Convolutional Neural Network, Deep Learning, Non-verbal communication.

Recognition of Human Emotion using Radial Basis Function Neural Networks with Inverse Fisher Transformed Physiological Signals

Emotion is a complex state of human mind influenced by body physiological changes and interdependent external events thus making an automatic recognition of emotional state a challenging task. A number of recognition methods have been applied in recent years to recognize human emotion. The motivation for this study is therefore to discover a combination of emotion features and recognition method that will produce the best result in building an efficient emotion recognizer in an affective system. We introduced a shifted tanh normalization scheme to realize the inverse Fisher transformation applied to the DEAP physiological dataset and consequently performed series of experiments using the Radial Basis Function Artificial Neural Networks (RBFANN). In our experiments, we have compared the performances of digital image based feature extraction techniques such as the Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and the Histogram of Images (HIM). These feature extraction techniques were utilized to extract discriminatory features from the multimodal DEAP dataset of physiological signals. Experimental results obtained indicate that the best recognition accuracy was achieved with the EEG modality data using the HIM features extraction technique and classification done along the dominance emotion dimension. The result is very remarkable when compared with existing results in the literature including deep learning studies that have utilized the DEAP corpus and also applicable to diverse fields of engineering studies.

Pain and Pleasure in Kamala Das’ My Story

Pain and Pleasure are the two psychological terms, used in English language and literature to show or describe the state of human mind with the perception of occurring incidents in their lives. In the relation of meaning, both of the terms are adversative adjectives to each other but both have the most important elements in the existence of human beings in the absence of each other. Pain describes the dark side of life or mental the state of human sufferings while pleasure is expressed during the mental state of joy and entertainment or light side of the life. Both of the terms have been well expressed in the autobiographical work of Kamala Das, My Story published in February 1973. This paper is going to explore the human state of mind in the most autobiographical work of Kamala Das, the female Keats and Lawrence of Indian English literature.

Evaluation of Semi-Diasporic State of Human Mind in the Selected Short Stories by Anita Desai: A Comparative Study

Evaluation of Semi-Diasporic State of Human Mind in the Selected Short Stories by Anita Desai: A Comparative Study

Quantum Computational Techniques for Prediction of Cognitive State of Human Mind from EEG Signals

The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems. The states of mind or judgment outcomes are highly complex phenomena that happen inside the human body. Decoding these states is significant for improving the quality of technology and providing an impetus to scientific research aimed at understanding the functioning of the human mind. One of the key advantages of quantum wave-functions over conventional classical models is the existence of configurable hidden variables, which provide more data density due to its exponential state-space growth. These hidden variables correspond to the amplitudes of each probable state of the system and allow for the modeling of various intricate aspects of measurable and observable physical quantities. This makes the quantum wave-functions powerful and felicitous to model cognitive states of the human mind, as it inherits the ability to efficiently couple the current context with past experiences temporally and spatially to approach an appropriate future cognitive state. This paper implements and compares some techniques like Variational Quantum Classifiers (VQC), quantum annealing classifiers, and hybrid quantum-classical neural networks, to harness the power of quantum computing for processing cognitive states of the mind by making use of EEG data. It also introduces a novel pipeline by logically combining some of the aforementioned techniques, to predict future cognitive responses. The preliminary results of these approaches are presented and are very encouraging with upto 61.53% validation accuracy.

Association between Locus of Control and Job Satisfaction in Employees: A Critical Review

Employee job satisfaction in the workplace is one of the important objectives of human resource managers and practitioners for better performance and productivity. Job satisfaction is a psychological state of human mind and it is subjective to individual experience and expectation, personality often plays an influential role. The term locus of control can be considered, a combination of one’s values, beliefs, past experiences, and cultural influence one’s life and behaviour. The internal-external locus of control constructs person’s internal expectancies for control of reinforcement. Further, it is believed that a person’s own behaviour determine the reinforcement they receive. The present research aims to investigate on the relationship between locus of control and job satisfaction and how does it influence, according to gender, culture, country, various sectors etc. The study will be based on different literature review in the area of concern. It will help us to develop a theoretical framework and a direction to solve for managerial problems of job satisfaction and performance of the employees. Dailey’s (1980) found that persons with an internal locus of control were more satisfied, motivated and had a high level of participation within their jobs. Some of the researches (Kircady et al., 2002; Noor, 2002; Martin et al., 2005; and Chen and Silverthorne, 2008) revealed that, there is a relationship between job satisfaction and locus of control. The present paper will be fulfilled with the gap of various dimensions such as how it is different according to gender, country, culture, and also in different sectors.

Oscillating Agent Model: Quantum Approach

Enabled by recent neuroscience and especially electroencephalogram (EEG) studies, this paper describes a conceptually novel modeling approach, based on quantum theory, to basic human mind states as systems of coherent oscillation. The aim is to bridge the gap between fundamental theory, experimental observation and the simulation of agents’ mind states. The proposed approach, i.e. an oscillating agent model (OAM), reveals possibilities of employing wave functions and quantum operators for a stylized description of basic mind states and the transitions between them. In the OAM the basic mind states are defined using experimentally observed EEG spectra, i.e. brainwaves (delta, theta, alpha, beta and gamma), which reveal an oscillatory nature of agents’ mind states. Such an approach provides an opportunity to model the dynamics of basic mind states by employing stylized oscillation-based representations of the characteristic EEG power spectral density (PSD) distributions of brainwaves observed in experiments. In other words, the proposed OAM describes a probabilistic mechanism for transitions between basic mind states characterized by unique sets of brainwaves. The instantiated theoretical framework is pertinent not only for the simulation of the individual cognitive and behavioral patterns observed in experiments, but also for the prospective development of OAM-based multi-agent systems.

Evaluation of human state of mind with pupillary responses to emotional pictures

Evaluation of human state of mind with pupillary responses to emotional pictures

気温と騒音が青年女性の快適感と受容感に及ぼす影響

The purpose of this study is to propose new environmental standards by reflecting the combined effect of temperature and noise stress on the human state of mind. Twenty-three female students were exposed to twenty combined conditions involving four hotter temperature levels and five noise levels in summer, and twenty-one of them were also exposed to combined four lower temperature levels and similar five nose levels in winter. The subjects reported their sensations regarding each combined condition. The equi-comfort and equi-acceptance charts for respective seasons and exposure periods were derived on the basis of the results. The contour lines of the equal universal comfort and acceptance sensations, respectively, were drawn in the charts with temperature and noise levels on the axes. These two kinds of charts can indicate the combined conditions which would be acceptable but not comfortable, as well as the grades of human comfortableness and acceptableness by themselves within this experimental condition.

Effects of drowsiness on evoked response

Event Abstract Back to Event Effects of drowsiness on evoked response Jiri Coufal1* and M. Mojžíš1 1 University of West Bohemia, Czechia In this paper we address major problem of present time – traffic accidents. They are caused by various factors. We are focusing on one of them – the driver’s ability to stay vigilant and resist the fatigue. We are exploring a relation of vigilance level with brain activity. Among many methods of brain activity monitoring we have chosen the electroencephalography. In healthy subjects brain potentials keep stable form when no external impulse is present. When the external impulse comes, human brain activity waveform is altered. Such response is called event related potential or evoked potential. Our primary objective is to evaluate subject’s vigilance level on the basis of the latency time when the impulse is processed with brain cognitive centers. The foreshadowed method of vigilance evaluation has to be proven. For that purpose we use scenarios resembling real traffic situations. The subject drives a vehicle on a monotonous road. His ride is interrupted by various obstacles suddenly appearing in front of him. The scenario is implemented in sophisticated simulation software. The simulator has to offer immersive experience for the test subject and its control has to be simple enough not to distract him. It also provides wide range of modification possibilities including powerful scripting language. The progress of the simulation along with continuous brain activity recording is the subject of an analysis and classification. That is done by wavelet transformation as a method of input signal analysis and processing. The result of the method is length of an interval between impulse and brain response. Apart from that, other methods of signal analysis and feature extraction are experimented with. Most important required qualities are time consumption of the calculation (it has to work online) and precision. The secondary objective is to extend the experiment using a feedback. Ascertained level of vigilance is used as parameter affecting course of the scenario. For example its fluctuation modifies the interval between obstacle re-spawn. Consecutive change of subject’s attention level is subject of our research as well. Our simulation/analysis framework is designed for wide range of experiments involving traffic situation and their impact on state of human mind. In full paper we are going to present results arising from its online operation. Conference: 10th International Conference on Cognitive Neuroscience, Bodrum, Turkey, 1 Sep - 5 Sep, 2008. Presentation Type: Poster Presentation Topic: Neuroinformatics of Cognition Citation: Coufal J and Mojžíš M (2008). Effects of drowsiness on evoked response. Conference Abstract: 10th International Conference on Cognitive Neuroscience. doi: 10.3389/conf.neuro.09.2009.01.324 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 15 Dec 2008; Published Online: 15 Dec 2008. * Correspondence: Jiri Coufal, University of West Bohemia, Pilsen, Czechia, jcoufal@kiv.zcu.cz Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jiri Coufal M. Mojžíš Google Jiri Coufal M. Mojžíš Google Scholar Jiri Coufal M. Mojžíš PubMed Jiri Coufal M. Mojžíš Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

言語表現が困難な障害者のための音圧指示による知能機械の運動制御

An interface between a human and an intelligent machine in a shared daily space, in which the intelligent machine follows instructions by the sound pressure of an ordinary human voice, is considered here. This interface is effective for the disabled who have difficulty with linguistic expression. The intelligent machine's cooperative movements are required to take account of the human's state of mind to serve effectively as his or her companion. In this paper, the patterns of intelligent machine motion are determined by adopting transfer functions that consider sound pressure directing operation as input, and the target speed of the intelligent machine as output. By changing transfer functions or adjustable parameters, different human psychological influences are expected. Motion properties of intelligent machines, which are appropriate for human states of mind, are examined using the SD method. As a result, it is clarified that human emotions concerning the intelligent machine motion are represented by the factors of “mild”, “lively”, “comfortable”and “interesting”. In addition, two patterns of transfer function and parameter th at can form desirablemovements considering human psvchology are identified.