scholarly journals Recognition of One’s Own Mental State by Parents of Children-patients

2017 ◽  
Vol 6 (2) ◽  
pp. 64-82
Author(s):  
V.K. Solondaev ◽  
E. Koneva ◽  
L. Lyasnikova
Keyword(s):  
Machine Learning ◽  
Medical Care ◽  
Mental State ◽  
Emotional Valence ◽  
Mental States ◽  
State Funding ◽  
Second Stage ◽  
Child Patients ◽  
Two Stages ◽  
Good Medical Care

In present article studied recognition of the situation of actualization of the mental state by child-patients parent's. The research was carried out in two stages. Sample: 70 mothers having children under the age of 7 years. Average age of mothers – 27 years. At the first stage by means of the technique "A relief of a mental state" (Prokhorov, 1998) were received estimates of mental states in the following situations: hospitalization of the child, "bad" medical care, troubles at work, "good" medical care, a usual (background) mental state. Based on the results of the first stage were two sets of descriptions were constructed. At the second stage, these sets were presented for recognition to the same sample. The first set of descriptions was constructed by the tequnique of the machine learning, the second set was constructed by the scheme of E.Yu. Artemyeva (1999). The results show the possibility of recognizing the situations of actualization of the mental state by the parents of child patients, which is limited by the emotional valence of the situation. Differentiation of situations of one valence is obstructed. The condition, actualized in one situation, can be extrapolated to other situations of similar emotional valence. The way to construct a description of the experienced mental state has a matter for recognition of the situation of actualization of a mental state. Funding This work was supported by grant RFH №15-06-10641.

Of Mental States and Machine Learning

2016 ◽  
Vol 60 (1) ◽  
pp. 1362-1366 ◽  
Author(s):  
Andrew Best ◽  
Samantha F. Warta ◽  
Katelynn A. Kapalo ◽  
Stephen M. Fiore
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Cognition ◽  
Mental State ◽  
Response Times ◽  
Interdisciplinary Approach ◽  
Mental States ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Machine Learning Classifiers

Using research in social cognition as a foundation, we studied rapid versus reflective mental state attributions and the degree to which machine learning classifiers can be trained to make such judgments. We observed differences in response times between conditions, but did not find significant differences in the accuracy of mental state attributions. We additionally demonstrate how to train machine classifiers to identify mental states. We discuss advantages of using an interdisciplinary approach to understand and improve human-robot interaction and to further the development of social cognition in artificial intelligence.

scholarly journals SOCIAL REPRESENTATION OF RUSSIANS ABOUT DEBT

2020 ◽  
pp. 308-332
Author(s):  
М.А. Гагарина
Keyword(s):  
Empirical Study ◽  
Mental State ◽  
Scientific Literature ◽  
Social Representations ◽  
Social Representation ◽  
Empirical Testing ◽  
Moral Obligations ◽  
Second Stage ◽  
Two Stages ◽  
The Relationship

В работе рассматривается проблема соотношения научных и обыденных представлений о долге. Приведен обзор отечественных и зарубежных исследований социальных представлений о нем. Отмечается, что долг описывается как «обязанность» и как «взятое взаймы», показана недостаточная разработанность этой проблематики на данный момент. Эмпирической проверке подверглись следующие гипотезы: (1) содержание социальных представлений о долге включает представления, описывающие долг как заeм и долг как обязанность; (2) социальные представления о денежном и моральном долге могут иметь одинаковое содержание, но структура, которую они образуют, может различаться (включать сходные категории, но их различный порядок упоминания и частоту); (3) должники имеют менее рациональные социальные представления о долге, оправдывающие возможность несвоевременного возврата. Представлены результаты эмпирического исследования, включающего два этапа. На первом, с участием 120 респондентов, проведено изучение содержания социальных представлений, относящихся к денежному долгу и долгу как обязанности, и структуры, которую они образуют; на втором этапе исследования с участием 1156 респондентов, изучено содержание ядра социальных представлений о долге в контексте долгового поведения и выявлены различия представлений у должников и лиц без задолженностей. В результате показано, что социальные представления о долге согласуются с научными знаниями: долг - это психическое состояние, возникающее в ответ на необходимость исполнения как денежных, так и моральных обязательств. Социальные представления о денежном и моральном долге включают сходные категории, но их различный порядок упоминания и частоту. При рассмотрении социальных представлений о долге в контексте долгового поведения уместно говорить об их рациональности - представления о денежном долге характеризуются осознанием собственной ответственности и необходимости исполнения обязательств, а также признанием важности расчетов стоимости займа и поиска наиболее выгодных вариантов. Должники тоже имеют представления о рациональном долговом поведении, но отличаются от респондентов, не прибегающих к заимствованию, и заемщиков, исполняющих свои долговые обязательства в полном объеме, приоритетом комфортного образа жизни и допустимостью отложенного исполнения своих долговых обязательств. The paper addresses the problem of the relationship between scientific and social representations of debt. It is shown that in the scientific literature debt is described as duty and as a loan. An overview of domestic and foreign studies of social representations is given, and insufficient development of social representations of debt is shown. The following hypotheses were subjected to empirical testing: (1) social representations of debt contain components that describe debt as a loan and debt as a duty; (2) social representations about monetary debt and moral debt have the same content, but different structure; (3) debtors have less rational representations about debt. The results of an empirical study involving two stages are presented, in the first stage with the participation of 120 respondents, a comparison of the content and structure of social representations of debt as a duty and monetary is carried out; At the second stage, with the participation of 1,156 respondents, the rationality of social representations of debt of Russians with different borrowing experiences, was studied. As a result, it is shown that social representations of debt are consistent with scientific knowledge: debt is a mental state arising in response to the need to fulfill both monetary and moral obligations. Social representations of debt are characterized by an awareness of own responsibility and the necessity to fulfill obligations, as well as rationality - recognition of the importance of calculating the value of the loan and finding the most profitable options. Debtors also have ideas of rational debt behavior, but differ from respondents who do not resort to borrowing and borrowers who perform their debt obligations in full, by the priority of a comfortable lifestyle and the admissibility of delayed performance of their debt obligations.

scholarly journals Discovering Hidden Mental States in Open Multi-Agent Systems by Leveraging Multi-Protocol Regularities with Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5198
Author(s):  
Emilio Serrano ◽  
Javier Bajo
Keyword(s):  
Machine Learning ◽  
Mental State ◽  
Ambient Intelligence ◽  
Smart Cities ◽  
Mental States ◽  
Multi Agent Systems ◽  
Great Cost ◽  
Agent Systems ◽  
Multi Agent ◽  
State Models

The agent paradigm and multi-agent systems are a perfect match for the design of smart cities because of some of their essential features such as decentralization, openness, and heterogeneity. However, these major advantages also come at a great cost. Since agents’ mental states are hidden when the implementation is not known and available, intelligent services of smart cities cannot leverage information from them. We contribute with a proposal for the analysis and prediction of hidden agents’ mental states in a multi-agent system using machine learning methods that learn from past agents’ interactions. The approach employs agent communication languages, which is a core property of these multi-agent systems, to infer theories and models about agents’ mental states that are not accessible in an open system. These mental state models can be used on their own or combined to build protocol models, allowing agents (and their developers) to predict future agents’ behavior for various tasks such as testing and debugging them or making communications more efficient, which is essential in an ambient intelligence environment. This paper’s main contribution is to explore the problem of building these agents’ mental state models not from one, but from several interaction protocols, even when the protocols could have different purposes and provide distinct ambient intelligence services.

Altered Fine Structure of B Lymphocytes Correlated with Defective Terminal Differentiation

1973 ◽  
Vol 31 ◽  
pp. 386-387
Author(s):  
Dale E. Bockman ◽  
L. Y. Frank Wu ◽  
Alexander R. Lawton ◽  
Max D. Cooper
Keyword(s):  
Immune Deficiency ◽  
B Lymphocytes ◽  
Plasma Cells ◽  
Present Report ◽  
Specific Antigen ◽  
Terminal Differentiation ◽  
Second Stage ◽  
Two Stages ◽  
Deficiency Diseases ◽  
Cell Line Generation

B-lymphocytes normally synthesize small amounts of immunoglobulin, some of which is incorporated into the cell membrane where it serves as receptor of antigen. These cells, on contact with specific antigen, proliferate and differentiate to plasma cells which synthesize and secrete large quantities of immunoglobulin. The two stages of differentiation of this cell line (generation of B-lymphocytes and antigen-driven maturation to plasma cells) are clearly separable during ontogeny and in some immune deficiency diseases. The present report describes morphologic aberrations of B-lymphocytes in two diseases in which second stage differentiation is defective.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Analisis Kebutuhan Petunjuk Praktikum Berbasis Keterampilan Proses Sains untuk Mencapai Kemampuan Merancang Eksperimen

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Fitriah Khoirunnisa ◽  
Friska Septiani Silitonga ◽  
Veri Firmansyah
Keyword(s):  
Core Competence ◽  
Process Skills ◽  
Learning Materials ◽  
Science Process Skills ◽  
Second Stage ◽  
The Core ◽  
The Core Competence ◽  
Two Stages ◽  
Practical Instruction ◽  
Design Experiments

Penelitian ini bertujuan menganalisis kebutuhan petunjuk praktikum berbasis Keterampilan Proses Sains (KPS) untuk mencapai kemampuan merancang eksperimen pada materi kalor reaksi kalorimetri. Penelitian dilakukan terhadap peserta didik kelas XI SMA Negeri 2 Kota Tanjungpinang. Variabel penelitian mencakup analisis kebutuhan bahan ajar dan analisis kesesuaian Kompetensi Inti (KI) dan Kompetensi Dasar (KD). Jenis penelitian yang dilakukan adalah penelitian deskriptif kualitatif. Tahapan pertama dalam penelitian ini adalah menganalisis kebutuhan bahan ajar dengan cara membandingkan dua petunjuk praktikum yang selama ini telah digunakan di sekolah tersebut, ditinjau dari aspek struktur format penulisan, aspek kreativitas, dan aspek keterampilan proses sains yang terdapat dalam petunjuk praktikum. Sehingga didapatkan kesimpulan bahwa petunjuk praktikum yang selama ini digunakan tidak memberikan kesempatan kepada peserta didiknya untuk merancang eksperimen yang telah ditentukan. Tahapan kedua yaitu menganalisis kesesuaian kompetensi inti dan kompetensi dasar, yang bertujuan untuk menentukan indikator pencapaian kompetensi (IPK) yang akan menjadi acuan dalam mengembangkan petunjuk praktikum berbasis keterampilan proses sains. Dari kedua tahapan yang telah dilakukan maka dapat disimpulkan bahwa peserta didik memerlukan petunjuk praktikum yang mampu mengonstruksi pikiran dan mengaktifkan kinerja mereka, sehingga pendekatan Keterampilan Proses Sains menjadi pilihan dalam mengembangkan petunjuk praktikum yang sesuai dengan karakteristik kurikulum 2013.   This research aims to analyze the needs of Science Process Skills based Practical Instruction to achieve the ability to design experiments on the calor of reaction. This research was done to the students of class XI SMA Negeri 2 Tanjungpinang City. Research Variable includes the analysis of the needs of the learning materials and analysis of the suitability of the Core Competence (KI) and Basic Competence (KD). The type of research conducted is descriptive qualitative research. The first stages in this research is to analyze the needs of learning materials by comparing two practical instructions that had been implementing in the school, from the aspects of the structure of writing format, creativity, and science process skills embedded in practical instructions. The conclusion of this research that current practical instructions does not give an opportunity to the participants to design determined experiments. The second stage, namely analyzing the suitability of core competence and basic competence, which aims to determine the indicators of achievement of the competencies (GPA) which will be a reference in developing process skills-based teaching instructions science. Of the two stages that has been done then it can be concluded that learners need practical instructions to construct  thinking and and their performance, so the Science Process Skills approach is an option in developing practical instruction suitable for the characteristics of the curriculum of 2013.

ESTIMATING GAS AND ROCK OUTBURST RISK ON THE BASIS OF KNOWLEDGE AND EXPERIENCE – THE EXPERT SYSTEM BASED ON FUZZY LOGIC

2014 ◽  
Vol 59 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Norbert Skoczylas
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Mining Industry ◽  
Automatic Assessment ◽  
Science Field ◽  
Second Stage ◽  
Two Stages ◽  
Outburst Hazard

Abstract The Author endeavored to consult some of the Polish experts who deal with assessing and preventing outburst hazards as to their knowledge and experience. On the basis of this knowledge, an expert system, based on fuzzy logic, was created. The system allows automatic assessment of outburst hazard. The work was completed in two stages. The first stage involved researching relevant sources and rules concerning outburst hazard, and, subsequently, determining a number of parameters measured or observed in the mining industry that are potentially connected with the outburst phenomenon and can be useful when estimating outburst hazard. Then, the Author contacted selected experts who are actively involved in preventing outburst hazard, both in the industry and science field. The experts were anonymously surveyed, which made it possible to select the parameters which are the most essential in assessing outburst hazard. The second stage involved gaining knowledge from the experts by means of a questionnaire-interview. Subjective opinions on estimating outburst hazard on the basis of the parameters selected during the first stage were then systematized using the structures typical of the expert system based on fuzzy logic.

Stable neural representations of mental states across target people and stimulus modalities

2020 ◽  
Author(s):  
Miriam E. Weaverdyck ◽  
Mark Allen Thornton ◽  
Diana Tamir
Keyword(s):  
Mental State ◽  
Mental States ◽  
Brain Regions ◽  
The Self ◽  
Similarity Analysis ◽  
Multiple Modalities ◽  
Neural Representations ◽  
Conceptual Core ◽  
Stimulus Modalities ◽  
Huge Variety

Each individual experiences mental states in their own idiosyncratic way, yet perceivers are able to accurately understand a huge variety of states across unique individuals. How do they accomplish this feat? Do people think about their own anger in the same ways as another person’s? Is reading about someone’s anxiety the same as seeing it? Here, we test the hypothesis that a common conceptual core unites mental state representations across contexts. Across three studies, participants judged the mental states of multiple targets, including a generic other, the self, a socially close other, and a socially distant other. Participants viewed mental state stimuli in multiple modalities, including written scenarios and images. Using representational similarity analysis, we found that brain regions associated with social cognition expressed stable neural representations of mental states across both targets and modalities. This suggests that people use stable models of mental states across different people and contexts.

Mental state dynamics explain the origin of mental state concepts

2020 ◽  
Author(s):  
Mark Allen Thornton ◽  
Milena Rmus ◽  
Diana Tamir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mental State ◽  
Mental States ◽  
Social World ◽  
The Social ◽  
Artificial Neural ◽  
Ebb And Flow

People’s thoughts and feelings ebb and flow in predictable ways: surprise arises quickly, anticipation ramps up slowly, regret follows anger, love begets happiness, and so forth. Predicting these transitions between mental states can help people successfully navigate the social world. We hypothesize that the goal of predicting state dynamics shapes people’ mental state concepts. Across seven studies, when people observed more frequent transitions between a pair of novel mental states, they judged those states to be more conceptually similar to each other. In an eighth study, an artificial neural network trained to predict real human mental state dynamics spontaneously learned the same conceptual dimensions that people use to understand these states: the 3d Mind Model. Together these results suggest that mental state dynamics explain the origins of mental state concepts.

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