Visual Representation and Cognitive Models in Information Visualization

As a branch of computer science,information visualization aims to help users understand and analyze complex data through graphical interfaces and interactive technologies.Information visualization primarily includes various visual structures such as time-series structures,spatial relationship structures,statistical distribution structures,and geographic map structures,each with unique functions and application scenarios.To better explain the cognitive process of visualization,researchers have proposed various cognitive models based on interaction mechanisms,visual perception steps,and novice use of visualization.These models help understand user cognition in information visualization,enhancing the effectiveness of data analysis and decision-making.

APPLICATION OF HUMAN COGNITIVE MODEL FOR TIME DEPENDENT OPERATOR BEHAVIOR IN CHINESE NPP

This paper discusses some issues on human reliability model of time dependent human behavior. Some results of the crew reliability experiment on Tsinghua training simulator in China are given, Meanwhile, a case of calculation for human error probability during anticipated transient without scram (ATWS) based on the data drew from the recent experiment is offered.

Research on cognitive model of English major writing course based on the Internet

With the development of Internet technology, network is more and more common in the teaching process. On the analysis of teaching status of English major writing course and in the guide of cognitive linguistics and constructivism, the paper studies the use of the internet in English major writing teaching cognitive model.

On Constructing the Cognitive Model of Knowledge Acquisition in Globalization-Oriented Study

This paper which is directed to constructing the cognitive model of knowledge acquisition in view of the increasing need of globalization-oriented study, points out features of globalization-oriented study, and proposes the importance and necessity for constructing the CMKAGS （cognitive model of knowledge acquisition in globalization-oriented study） which includes attention, background knowledge, and chunking memory that involves semantic chunking, information encoding as well as choice of information encoding. The cognitive model of knowledge acquisition in question aims at helping e-learners to memorize their learned knowledge and improve their studies effectively and efficiently, whether they study in enhanced conditions or in natural conditions

Holistic Discourse in the Network Cognitive Modeling

The purpose of this paper is to pose a new question to speed-up mutual understanding among team members or/and group of experts when communicating over the Internet in forms of virtual collaboration, electronic brainstorming, network strategic conversation, etc. We have previously proposed an approach that the convergent control mechanism based on the fundamental principles of thermodynamic and inverse problem solution method, as well as various artificial intelligence techniques, be incorporated into the communicative process. This paper shows a further development of the approach in terms of applying The Fuzzy Tychonoff Theorem along with quantum techniques provide to reach a high level of holistic discourse which is achieved not only through the application of fundamental principles of compactness of the topological space, but also utilizing quantum entanglement and complementarity principles for discourse structuring in a special way. The approach is implemented as the Responsibility Thinking System （RTS） tested in the course of finding the decisions of the real life issues.

Cognitive Model of the Journalist on Journalism under Newspaper Industry Transformation Period

In this paper, we conduct research on cognitive model of the journalist on journalism under newspaper industry transformation period. “Journalism” is the logical starting point of theoretical journalism system that is the most abstract, general concept of journalism. Therefore, analyzing the connotation and the essence of “news activities” has special significance and value to the whole journalism, that especially theoretical journalism. All of these activities, together with the news dissemination activities as the core of the news activities, are the broad news activities. From the perspective of journalism, these activities constitute the main object of the study of journalism, news on different areas of activity, form different areas or different branches of journalism. For this, the paper proposes the cognitive model of the journalist on the journalism under newspaper industry transformation period that will promote the further development.

Idealized cognitive models for students to strengthen the effect of music imagination

If there is no imagination, there is no music appreciation. It should create imaginary world for students in the music classroom teaching practice, and it should foster the students＇ musical imagination. Thus, colleagues can make discussion about strategies proposed including pilot background, context led, screen hygiene conditions and others. We use Cognitive Linguistic Theories to introduce idealized cognitive model and its theoretical basis and the intensified impact on student musical imagination.

THEORY AND IMPLEMENTATION OF ICAI COGNITIVE MODEL BASED ON MEANING

Cognitive models must be able to adapt the students learning behaviors dynamically.In our point of view,the processes of learning and understanding are,in nature,the procedure that gains the meaning of the object to be learned.So,ICAI cognitive models should reflect the meaning structure of the domain knowledge in students mind.According to this view,we developed the meaning theory of Ludwig Wittgenstein,and proposed the concept of meaning conjoinism.On the basis of the meaning conjoinism we proposed a meaning oriented ICAI cognitive model and its corresponding teaching tactics.Furthermore,we developed an ICAI system named Thinking and the efficiency of our proposal has been demonstrated.

C-HMAX: Artificial Cognitive Model Inspired by the Color Vision Mechanism of the Human Brain

Artificial cognitive models and computational neuroscience methods have garnered great interest from both neurologist and leading analysts in recent years. Among the cognitive models, HMAX has been widely used in computer vision systems for its robustness shape and texture features inspired by the ventral stream of the human brain. This work presents a Color-HMAX （C-HMAX） model based on the HMAX model which imitates the color vision mechanism of the human brain that the HMAX model does not include. C-HMAX is then applied to the German Traffic Sign Recognition Benchmark （GTSRB） which has 43 categories and 51 840 sample traffic signs with an accuracy of 98.41%, higher than most other models including linear discriminant analysis and multi-scale convoiutional neural network.

A cognitive model＇s view of animal cognition

Although it is a relatively new field of study, the animal cognition literature is quite extensive and difficult to synthesize. This paper explores the contributions a comprehensive, computational, cognitive model can make toward organizing and assimilating this literature, as well as toward identifying important concepts and their interrelations. Using the LIDA model as an example, a framework is described within which to integrate the diverse research in animal cognition. Such a framework can provide both an ontology of concepts and their relations, and a working model of an animal＇s cognitive processes that can compliment active empirical research. In addition to helping to account for a broad range of cognitive processes, such a model can help to comparatively assess the cognitive capabilities of different animal species. After deriving an ontology for animal cognition from the LIDA model, we apply it to develop the beginnings of a database that maps the cognitive facilities of a variety of animal species. We conclude by discussing future avenues of research, particularly the use of computational models of animal cognition as valuable tools for hypotheses generation and testing [Current Zoology 57 （4）： 499-513, 2011].

Design of Simulation Competition Platform Based on Cognitive Behavior Modeling

Cognitive behavior modeling of agent is an important component of simulation system,and there are some difficulties in the simulation of course teaching.When students make simulation experiments about cognitive behavior modeling,such as algorithm design and model construction,there is no simulation competition platform that is controllable,flexible and scalable.To solve this problem,we propose a simulation competition platform based on cognitive behavior modeling,called TankSim,for undergraduate and graduate students.This platform aims to cultivate studenfs team collaboration and innovation capability,and improve their learning motivation.This paper elaborates the proposed platform from three aspects,including demand analysis,platform design,and content design.

A Cognitive Science Framework for the Analysis of Knowledge-Based Systems

The paper presents a cognitive science framework for the analysis of knowledge-based systems,including people, media. simulation and expert systems, resulting in a practical model for the procedures ofknowledge engineering. Starting with the construct of a social organization model driven by anticipationand thed differentiating this into pesonal scientists with diverse relations to people and their internal andexternal communication, it provides powerful and general model of society. people, and the roles of peoplein society. This model extends naturally ic the role of conventional media in the knowledge processes ofsociety and the new roles of computer-based simulation and expert systems. In particular it provides amodel of knowledge transfer that enables the processes of knowledge engineering to be analyzed andautomated.

Modeling the Social Reinforcement of Misinformation Dissemination on Social Media

Despite the salience of misinformation and its consequences, there still lies a tremendous gap in research on the broader tendencies in collective cognition that compels individuals to spread misinformation so excessively. This study examined social learning as an antecedent of engaging with misinformation online. Using data released by Twitter for academic research in 2018, Tweets that included URL news links of both known misinformation and reliable domains were analyzed. Lindström’s computational reinforcement learning model was adapted as an expression of social learning, where a Twitter user’s posting frequency of news links is dependent on the relative engagement they receive in consequence. The research found that those who shared misinformation were highly sensitive to social reward. Inflation of positive social feedback was associated with a decrease in posting latency, indicating that users that posted misinformation were strongly influenced by social learning. However, the posting frequency of authentic news sharers remained fixed, even after receiving an increase in relative and absolute engagement. The results identified social learning is a contributor to the spread of misinformation online. In addition, behavior driven by social validation suggests a positive correlation between posting frequency, gratification received from posting, and a growing mental health dependency on social media. Developing interventions for spreading misinformation online may profit by assessing which online environments amplify social learning, particularly the conditions under which misinformation proliferates.

Information Comprehension Based on the New Knowledge Acquired by Cognitive Subject as the Fourth Dimension Set

On the basis of studying general comprehension model of information, this paper puts forward the Four Dimensions Set Information Comprehension Model （FDSICM） based on regarding the new knowledge acquired by cognitive subject as the fourth dimension set. Making use of the Four Dimension Set Information Comprehension Model （FDSICM）, this paper analyzes the information attributes and expatiates from three levels the comprehension of the information meaning.

On Performance Gauge of Average Multi-Cue Multi-Choice Decision Making:A Converse Lyapunov Approach

Motivated by the converse Lyapunov technique for investigating converse results of semistable switched systems in control theory,this paper utilizes a constructive induction method to identify a cost function for performance gauge of an average,multi-cue multi-choice(MCMC),cognitive decision making model over a switching time interval.It shows that such a constructive cost function can be evaluated through an abstract energy called Lyapunov function at initial conditions.Hence,the performance gauge problem for the average MCMC model becomes the issue of finding such a Lyapunov function,leading to a possible way for designing corresponding computational algorithms via iterative methods such as adaptive dynamic programming.In order to reach this goal,a series of technical results are presented for the construction of such a Lyapunov function and its mathematical properties are discussed in details.Finally,a major result of guaranteeing the existence of such a Lyapunov function is rigorously proved.

Natural Scene Classification Inspired by Visual Perception and Cognition Mechanisms

The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natural scene image classification.This approach consists of one visual perception model and two visual cognition models.The visual perception model,composed of two steps,is used to extract discriminative features from natural scene images.In the first step,we mimic the oriented and bandpass properties of human primary visual cortex by a special complex wavelets transform,which can decompose a natural scene image into a series of 2D spatial structure signals.In the second step,a hybrid statistical feature extraction method is used to generate gist features from those 2D spatial structure signals.Then we design a cognitive feedback model to realize adaptive optimization for the visual perception model.At last,we build a multiple semantics based cognition model to imitate human cognitive mode in rapid natural scene categorization.Experiments on natural scene datasets show that the proposed method achieves high efficiency and accuracy for natural scene classification.

A Novel Interference Modeling Scheme in Cognitive Networks

In this paper,we propose a mathematical model of aggregate co-channel interference over Rayleigh fading in cognitive networks.Unlike the statistical models in the literature that aim at finding the bound or approximation of the interference,the proposed model gives an accurate expression of probability density function(PDF),cumulative distribution function(CDF) and mean and variance of the interference,which takes into account a number of factors,such as spectrum sensing scheme,and spatial distribution of the secondary users(SUs).In particular,we focus on a more general spatial structure where there are two roles of primary users(PUs)and the interfering SUs distributed in the two-dimensional space.The framework developed in this paper is easy to be applied in power control,error evaluation and other applications.

Adaptive level of autonomy for human-UAVs collaborative surveillance using situated fuzzy cognitive maps

Collaborating with a squad of Unmanned Aerial Vehicles(UAVs)is challenging for a human operator in a cooperative surveillance task.In this paper,we propose a cognitive model that can dynamically adjust the Levels of Autonomy(LOA)of the human-UAVs team according to the changes in task complexity and human cognitive states.Specifically,we use the Situated Fuzzy Cognitive Map(Si FCM)to model the relations among tasks,situations,human states and LOA.A recurrent structure has been used to learn the strategy of adjusting the LOA,while the collaboration task is separated into a perception routine and a control routine.Experiment results have shown that the workload of the human operator is well balanced with the task efficiency.

Systematic Safety Analysis Method for Power Generating Equipment

A systematic safety analysis method is presented to guide the whole analysis process starting with safety analysis requirement and ending with technical and economical evaluation of the knowledge model and the arrangement of sensors. The method consists of five phases, including data acquisition on factual evidence and collecting design, manufacturing, and installation data of equipment; establishing knowledge model; measurable analysis and selection of sensors as well cost evaluation; knowledge description; and overall evaluation. The proposed method is used for safety analysis of hydraulic power generating units and the analysis results validate the method very well.