Brain Cognitive Science Research Articles

뇌 인지과학적 관점에서 본 영어 내러티브 읽기 교육에 대한 비판적 고찰

This paper explores the intersection of brain cognitive science and teaching English narrative reading, identifying three key brain characteristics observed during this process. Firstly, the brain fills in information gaps from incomplete sensory data. Secondly, it actively engages in imaginative and experiential involvement with the narrative, facilitated by the action-perception circuit and mirror neurons. Thirdly, narrative reading activates the Default Mode Network, prompting self-exploration. Despite these insights, textbooks, teaching methods, and assessments exhibit discrepancies, with a focus on didactic messages, grammar, and language testing. This paper advocates for a shift towards immersive narrative reading experiences by integrating brain cognitive science findings. It proposes four reading approaches—inferential, experiential, imagery, and scene reading. Bridging these gaps can enhance education by encouraging direct engagement, imaginative exploration, and alignment of assessments with teaching goals, fostering a deeper connection between students and narrative texts.

Brain Relatively Inert Network: Taking Adult Attention Deficit Hyperactivity Disorder as an Example.

Resting-state functional MRI (rs-fMRI) has been increasingly applied in the research of brain cognitive science and psychiatric diseases. However, previous studies only focused on specific activation areas of the brain, and there are few studies on the inactivation areas. This may overlook much information that explains the brain’s cognitive function. In this paper, we propose a relatively inert network (RIN) and try to explore its important role in understanding the cognitive mechanism of the brain and the study of mental diseases, using adult attention deficit hyperactivity disorder (ADHD) as an example. Here, we utilize methods based on group independent component analysis (GICA) and t-test to identify RIN and calculate its corresponding time series. Through experiments, alterations in the RIN and the corresponding activation network (AN) in adult ADHD patients are observed. And compared with those in the left brain, the activation changes in the right brain are greater. Further, when the RIN functional connectivity is introduced as a feature to classify adult ADHD patients from healthy controls (HCs), the classification accuracy rate is 12% higher than that of the original functional connectivity feature. This was also verified by testing on an independent public dataset. These findings confirm that the RIN of the brain contains much information that will probably be neglected. Moreover, this research provides an effective new means of exploring the information integration between brain regions and the diagnosis of mental illness.

Neural Network Data Mining Clustering Optimization Algorithm

Data mining technology is an effective way to solve the problem of rich data and poor knowledge. Cluster analysis is an important content of data mining, including analysis ideas based on partition, hierarchy, density, grid and model. Neural network clustering is a typical clustering method based on model thinking. It is an organic combination of brain cognitive science and data mining. It has a strong theoretical connection with actual brain processing knowledge. This paper uses the K-means algorithm to optimize the neural network clustering data mining algorithm, and designs experiments to verify the neural network data mining clustering optimization algorithm proposed in this paper. The experimental research results in this paper show that on four UCI datasets, the mean MP values of the neural network data mining clustering optimization algorithms are 77.9 and 87.72, respectively, which are greater than the values of the other two algorithms. This paper also applies the algorithm to the study of the distribution of remaining oil. The algorithm has achieved obvious results in the cluster analysis of the degree of flooding.

Status Identification and Identity Construction of Ethnic Minorities in the United States Based on Brain Cognitive Science

Cognition is a process in which the brain processes information including feelings, language, memory and other related activities. It has a direct relationship with the social environment. In recent decades, the number of immigrants in the United States has continued to rise, and the structure of the migrating ethnic groups has changed greatly. Most of them are Hispanic and Asian. These have brought new challenges and problems to the melting ability of ethnic minorities in the United States. Moreover, ethnic minorities also have the pursuit of their own status identity and seek ways to construct their own identity in the new cultural and political system. Based on the cognitive science, this paper takes the American minorities as research subjects, and studies the status identification and identity construction of the ethnic minorities in the United States, explains related factors influencing status identification and identity construction, and analyzes and predicts the current situation and the future of the minorities in the United States. Measures to maintain the status identity of ethnic minorities have been put forward so that to establish a sense of belonging to maintain the stable and harmonious development of the country. This paper enriches the research on the identification of ethnic minorities in the United States theoretically and provides reference for similar problems in other countries.

Impact Mechanism of Tourism Risk Perception Based on Psychological Theory and Brain Cognitive Science

In recent years, people's living standards are increasing continuously and people are paying more and more attention to leisure activities. As one of the most important ways of leisure activities, the tourist market is developing rapidly, which poses more stringent requirements for the improvement of problems and risk in tourism market. Based on psychological theory and brain cognitive science, this paper studies the mechanism of tourism risk perception through EEG experiments. The research results show that according to hypothesis 1 and hypothesis 2, the main effect between individuals preparing to travel and tourism decision-making behavior is significant due to personal preference, whether time is convenient or time preference. Based on the results of LPP data analysis, the hypothesis 3 can be verified. Those people who travel frequently are less capable of perceiving risks, which can also verify hypothesis 4. People believe those places with more perfect facilities are safer, so they have worse perception of risk. The research conclusions of this people are conducive to reducing tourism risks of tourists and can also help tourism agencies provide more appropriate and caring services for tourists.

Creative Teaching Model of Civil Engineering Classroom Based on Brain Cognitive Science

In the past ten years, the researches on brain science and neuropedagogy has developed rapidly, which has a revolutionary influence on education and teaching. The cross development of brain science, neuroscience and classroom teaching is the inevitable trend of education development. Based on the macro-micro principle of brain cognition and in combination with various factors that influence the teaching of civil engineering course, this study establishes a model of full time-space infiltration teaching system, and puts forward the teaching strategy on the basis of the teaching model, so as to improve and realize the scientific and high-efficient classroom teaching and promote the efficiency and effect of teaching.

Creative English Classroom Teaching Model Considering Brain Cognition Enhancement

In order to maximize the students' brain potential in English learning, this paper makes an investigation and analysis of brain-based college English teaching classroom, and proposes an English classroom teaching mode considering brain cognition enhancement method according to the investigation. By means of comprehensive analysis of the application of brain science theory and the teaching of brain science, in combination with the model of literature investigation and questionnaire survey, this paper summarizes the theoretical basis of brain cognitive science and explains its application in college English teaching. This paper puts forward the strategies and suggestions of English classroom innovation based on brain science, so that English teaching classroom can be innovated and transformed according to the laws of brain cognition as much as possible, arouse students' brain activity, and finally realize the better learning of English knowledge. On the one hand, this study provides reference for English classroom teaching based on brain science in colleges and universities, on the other hand, it has guiding significance for the reform of English teaching in colleges and universities.

A study on auditory sensitivity and behavioral cognition changes before and after drinking in psychological acoustics

In general, an auditory sensitivity after drinking is becoming insensitive and a perception of behavior becomes very poor. This can be explained by the characteristics of body changes due to alcohol absorption, and this study analyzed and studied these characteristics. This study provides the necessary bases for psychoacoustics and brain cognitive science by obtaining a measured data through cognitive testing of the characteristics of body changes before and after drinking. We analyzed the post-drinking changes in brain waves and performed psychoacoustic analysis of these characteristics.

An Innovative Thinking‐Based Intelligent Information Fusion Algorithm

This study proposes an intelligent algorithm that can realize information fusion in reference to the relative research achievements in brain cognitive theory and innovative computation. This algorithm treats knowledge as core and information fusion as a knowledge-based innovative thinking process. Furthermore, the five key parts of this algorithm including information sense and perception, memory storage, divergent thinking, convergent thinking, and evaluation system are simulated and modeled. This algorithm fully develops innovative thinking skills of knowledge in information fusion and is a try to converse the abstract conception of brain cognitive science to specific and operable research routes and strategies. Furthermore, the influences of each parameter of this algorithm on algorithm performance are analyzed and compared with those of classical intelligent algorithms trough test. Test results suggest that the algorithm proposed in this study can obtain the optimum problem solution by less target evaluation times, improve optimization effectiveness, and achieve the effective fusion of information.

A Cognitive view of the immune system

Abstract This paper proposes a definition of cognition as a system capable of both action and perception, in which the coupling of action and perception is such that (1) the system exhibits operational closure, and (2) the emergent behavior of the system in its environment satisfies a meaningful constraint. It is argued that the immune system of vertebrates meets the requirements of the definition, and hence that the immune system is a cognitive entity. The full articulation of the brain cognitive science also requires a study of emergent behavior at the level of meaningful interactions between a system endowed with closure and its environment. The objects of cognition, with or without neurones, are brought into existence by the coupled perceptions and actions of the cognitive system itself.