Human Processes Research Articles

The Expert System Determines Children's Emotions with Learning Achievement Using The Web-based Damster Shafer Method

Psychology studies human behavior and mental processes is possible there involved the use of technology, but the use and utilization of technology in the field of psychology is felt still lacking. One method that is still widely used in psychology that is by making the Demster Shafer sheet. Demster Shafer method can be modified in the form of computer applications, where the application is using the knowledge of experts of psychology entered into the computer called expert systems applications. Application of expert system is designed to determine the child's learning style at primary school age. Application of expert systems is expected to overcome the problems of determining the child's learning style. In addition, it can be used as a support in the field of psychology and can be used for public purposes and individuals in general.

Orthogonal-Moment-Based Attraction Measurement With Ocular Hints in Video-Watching Task

Pupil dilation and eye movements are closely related to human emotional and cognitive processes. Visual stimulus, especially video clips, is widely used in computer-assisted experimental paradigms as emotional inducers. However, the level of attraction as a critical factor to such visual stimulus still needs comprehensive investigation. This article conducts a novel study of attraction assessment with pupil diameter and eye movements. We collected high temporal resolution ocular variation data from 50 subjects while they viewed a variety of emotional video stimuli. Besides, this article proposes two orthogonal-moment-based feature extraction methods for emotion classification, i.e., Legendre moment and Krawtchouk moment. The results of experiments show that our proposed feature sets achieve better classification performance compared with conventional time-or frequency-domain feature sets. The accuracy of predicting attraction level reached 87.4% and 91.0% when new features were used alone and combined with conventional features, respectively. Compared with the conventional features with an accuracy of 86.6%, our proposed features can improve the accuracy by 4.4%.This study conducts a ground investigation of quantitative attraction assessment for video clips, which might provide reference for paradigm design of affective computing research.

Adoption and utilization of medical decision support systems in the diagnosis of febrile Diseases: A systematic literature review

Medical decision support systems (MDSS) utilized for medical diagnosis aim to improve patient care; they do so by simulating the human cognitive process in order to arrive at a valid clinical conclusion. Medical decision support systems play a significant role in promoting an efficient and effective health care system. However, the adoption and usage of these systems in daily medical practice have been slow due to a lack of physician confidence in the systems and concerns about the erosion of professional autonomy, obscure physicians’ role, and diminished physician-patient relationships. Despite successes in the development of decision support systems, there is still a low adoption and utilization of MDSSs. This study reviews the development, adoption, and utilization of MDSSs in diagnosing Febrile Diseases between 2008 - 2020. We carried out a systematic literature review on the adoption and utilization of MDSS in diagnosing Febrile Diseases using guidelines provided in literature for systematic reviews and a PRISMA flow to guide paper selection, inclusion, and exclusion criteria. Three reviewers independently carried out literature searches on selected databases for MDSS to diagnose febrile diseases. After the initial searches, the reviewers cross-checked the results, and disagreements were resolved through mutual agreements. The reviewers extracted the data based on an agreed template, and a separate set of reviewers cross-checked this. The extracted data was then synthesized, analyzed, and split into various themes to answer the research questions. We identified 113 papers on MDSS development and utilization and 15 papers on studies relating to the adoption of MDSS. The analysis revealed that Fuzzy Logic was the most prevalent model of MDSS in use, while most models discussed in the papers were unimplemented. The results show that the main reason for proposing these models was to improve accuracy and precision. The main factors influencing the adoption of these models by medical facilities were usefulness, relative advantage, and ease of use. However, the reasons for adopting MDSS by the medical facilities were not expressed in 50% of the studies. In contrast, most of those who adopted MDSS disclosed that it was used to improve effectiveness and adherence to guidelines. Finally, compared to WHO statistics, it was determined that the number of papers did not correspond to the disease burden in the research continents. Although considerable research has been carried out on the adoption and utilization of MDSS, there is a paucity of research on implemented models of MDSS. Despite the advances in MDSSs and their accuracy in diagnosing diseases, many developed models have remained unimplemented in clinical practice. Future studies should focus on encouraging the actual usage of existing models that have not been implemented and conducting research on diseases with a high burden in their context. Governments and medical facility managers need to implement new policies, guidelines, and a support structure that will encourage the adoption and actual usage of MDSS in daily medical practice.

MODERN LEXICOLOGY AND ITS MAIN BRANCHES

The field of linguistics, the scientific study of human natural language, is a growing and exciting area of study, with an important impact on fields as diverse as education, anthropology, sociology, language teaching, cognitive psychology, philosophy, computer science, neuroscience, and artificial intelligence, among others. Indeed, the last five fields cited, along with linguistics, are the key components of the field of cognitive science, the study of the structure and functioning of human cognitive processes. In spite of the importance of the field of linguistics, many people, even highly educated people, will tell you that they have only a vague idea of what the field is about. Some believe that a linguist is a person who speaks several languages fluently. What is linguistics, then? Fundamentally, the field is concerned with the nature of language and (linguistic) communication. It is apparent that people have been fascinated with language and communication for thousands of years, yet in many ways we are only beginning to understand the complex nature of this aspect of human life.

A Method for Predicting the Visual Attention Area in Real-Time Using Evolving Neuro-Fuzzy Models

This research paper presents the prediction of the visual attention area on a visual display using an evolving rule-based fuzzy model: evolving Takagi–Sugeno (eTS). The evolving fuzzy model is feasible for predicting the visual attention area because of its non-iterative, recursive, online, and real-time nature. Visual attention area prediction through a web camera is a problem that requires online adaptive systems with higher accuracy and greater performance. The proposed approach using an evolving fuzzy model to predict the eye-gaze attention area on a visual display in an ambient environment (to provide further services) mimics the human cognitive process and its flexibility to generate fuzzy rules without any prior knowledge. The proposed Visual Attention Area Prediction using Evolving Neuro-Fuzzy Systems (VAAPeNFS) approach can quickly generate compact fuzzy rules from new data. Numerical experiments conducted in a simulated environment further validate the performance and accuracy of the proposed model. To validate the model, the forecasting results of the eTS model are compared with DeTS and ANFIS. The result shows high accuracy, transparency and flexibility achieved by applying the evolving online versions compared to other offline techniques. The proposed approach significantly reduces the computational overhead, which makes it suitable for any sort of AmI application. Thus, using this approach, we achieve reusability, robustness, and scalability with better performance with high accuracy.

Impacts of digital game-based flipped teaching approach on learning effectiveness of the students with different prior knowledge levels

ABSTRACT This study proposed a game-based flipped teaching approach and applied it to a HTML (HyperText Markup Language) course. We developed two versions of the pre-class content testing, one of which was game-based, using a “looking-through” game, and the other which was traditional, using a multiple-choice test. We conducted a teaching experiment of 9 weeks with 3 h per week. The valid sample was 39 students in the experimental group using the game-based version, and 32 in the control group using the traditional version. We hoped that the students’ learning effectiveness could be increased. However, adding the game may have different impacts on different human factors and different cognitive processes of Bloom’s taxonomy. To more precisely understand these problems, we divided the students into high and low prior knowledge, and divided learning effectiveness into the higher-level applying and analyzing aspects and the lower-level remembering and understanding aspects. The results showed that learners with low prior knowledge who originally lacked the foundational knowledge became interested in concentrating and then in the higher-level cognitive learning processes because of the experience of immersing themselves in the flow of the game. The learning effectiveness of their classroom participation significantly improved.

The Role of Cognitive Control in Paranormal Beliefs: A Study Based on Performance in Go/No-go Task

Introduction: Cognitive control plays a role in human behavior and mental processes and affects paranormal beliefs. This study aims to investigate the role of cognitive control in paranormal beliefs using the go/no-go task. Methods: A total of 92 people were selected based on low, middle, and high scores in the revised paranormal belief scale (R-PBS) and assigned to 3 groups. The groups included 30 severe paranormal believers (13 females with a mean age of 25.3 years), 31 mild paranormal believers (14 females with a mean age of 26.4 years), and 31 skeptics (16 females with a mean age of 25.8 years). All participants were tested on the go/no-go task. A multivariate analysis of variance was conducted with the given groups (severe paranormal believers, mild paranormal believers, and skeptics) as the independent variable and the go/no-go subscales scores as dependent variables. Results: The findings showed a significant difference between the mean scores in errors of go (F(2, 89)=7.20, P=0.01), errors of no-go (F(2, 89)=11.81, P=0.01), and reaction time (F(2, 89)=21.46, P=0.01) between the groups. Conclusion: The severe and mild paranormal believers had lower accuracy and slower reaction times than the skeptics group. Therefore, severe paranormal believers and mild paranormal believers had a weakness in all go/no-go subscale scores. This finding suggests that paranormal beliefs may be related to poor cognitive control.

Mobile cognition: imaging the human brain in the 'real world'.

Cognitive neuroscience studies in humans have enabled decades of impactful discoveries but have primarily been limited to recording the brain activity of immobile participants in a laboratory setting. In recent years, advances in neuroimaging technologies have enabled recordings of human brain activity to be obtained during freely moving behaviours in the real world. Here, we propose that these mobile neuroimaging methods can provide unique insights into the neural mechanisms of human cognition and contribute to the development of novel treatments for neurological and psychiatric disorders. We further discuss the challenges associated with studying naturalistic human behaviours in complex real-world settings as well as strategies for overcoming them. We conclude that mobile neuroimaging methods have the potential to bring about a new era of cognitive neuroscience in which neural mechanisms can be studied with increased ecological validity and with the ability to address questions about natural behaviour and cognitive processes in humans engaged in dynamic real-world experiences.

Effect of auditory noise circumstance on visual images encoding based electroencephalography analysis

ABSTRACT Auditory stimulation may be beneficial or detrimental to human cognitive and memory processing. Numerous studies have been conducted to investigate the influence of various sounds on human performance. The current research discovers the impact of auditory white noise on the memory performance of 60 college students using different difficulty levels in visual object-number pair assessment. Memory perfomance is accessed based on the assessment recall scores and brain activities. An electroencephalography (EEG) device measures and records the brain's electrical activity during the experimental session. Nine EEG channel locations are selected for brain activities analysis. The raw EEG dataset is processed using wavelet methods of stationary wavelet transform (SWT) and discrete wavelet transfrom (DWT) using the Daubechies (db) function to eliminate the artifact components and extract the required EEG features. The result indicated that the college student remembered the object-number pair assessment better under auditory white noise rather than no audio condition. This contributed from the activation of alpha, theta, gamma, and beta activities that increased the subject's attention, alertness, and sensory processing during memorizing. Therefore, it is suggested that the auditory white noise should be listened to during remembering and learning visual items, which can boost memory performance.

Algorithmic Nudging: The Need for an Interdisciplinary Oversight

Nudge is a popular public policy tool that harnesses well-known biases in human judgement to subtly guide people’s decisions, often to improve their choices or to achieve some socially desirable outcome. Thanks to recent developments in artificial intelligence (AI) methods new possibilities emerge of how and when our decisions can be nudged. On the one hand, algorithmically personalized nudges have the potential to vastly improve human daily lives. On the other hand, blindly outsourcing the development and implementation of nudges to “black box” AI systems means that the ultimate reasons for why such nudges work, that is, the underlying human cognitive processes that they harness, will often be unknown. In this paper, we unpack this concern by considering a series of examples and case studies that demonstrate how AI systems can learn to harness biases in human judgment to reach a specified goal. Drawing on an analogy in a philosophical debate concerning the methodology of economics, we call for the need of an interdisciplinary oversight of AI systems that are tasked and deployed to nudge human behaviours.

Rising Popularity of Internet Memes in India: A Media Psychology Perspective

The psychology behind internet memes is complex and multi-faceted, drawing on various aspects of human behaviour and cognitive processes. Memes have become a part of the social media culture and its popularity continues to grow in India. The analysis of internet memes can provide valuable insights into the cultural, social, and psychological aspects of our online lives. In the field of media psychology, the impact of memes on human behaviour and cognition is a growing area of research. Researchers are exploring the ways in which memes can be used to shape social attitudes, as well as the psychological mechanisms behind their popularity and spread. Media psychology and memes are both important areas of study that shed light on the ways in which communication occurs in contemporary society. By examining the relationship between media messages and the psychological processes that underlie them, we can gain a better understanding of how communication shapes our attitudes, beliefs, and behaviours. In the context of memes, the emotions evoked by a meme can be amplified through its spread and can have a significant impact on public opinion and discourse. Based on qualitative analysis, the paper employs the Social Cognitive Theory along with Uses and Gratifications Theory to decode the manifestation of memes in our social system and creation of echo chambers.

Network QoS Impact on Spatial Perception through Sensory Substitution in Navigation Systems for Blind and Visually Impaired People

A navigation system for individuals suffering from blindness or visual impairment provides information useful to reach a destination. Although there are different approaches, traditional designs are evolving into distributed systems with low-cost, front-end devices. These devices act as a medium between the user and the environment, encoding the information gathered on the surroundings according to theories on human perceptual and cognitive processes. Ultimately, they are rooted in sensorimotor coupling. The present work searches for temporal constraints due to such human-machine interfaces, which in turn constitute a key design factor for networked solutions. To that end, three tests were conveyed to a group of 25 participants under different delay conditions between motor actions and triggered stimuli. The results show a trade-off between spatial information acquisition and delay degradation, and a learning curve even under impaired sensorimotor coupling.

A temporal graph framework for intelligence extraction in social media networks

Social media networks (SMNs) are increasingly used in professional management of knowledge workers and related assets. However, the factors affecting behavioral trends and activity levels in these networks are not well understood. Although social and cognitive theories can help to explain human behavior in traditional social networks, their application to SMNs has not been validated. Traditional social network modeling techniques may not accurately predict real-world SMN activities. This research developed a temporal graph framework for intelligence extraction in SMNs. Theory-based, data-driven models (Conformity Model (COM), Recency-Primacy Model (REM), Trend Interaction Model (TIM), Periodic Interaction Model (PIM)) were developed based on the framework to capture various aspects of user behavior: conformity effect, recency, primacy, periodicity, and dynamic trend. The models capture the activity history and dynamically combine pricing information to enhance predictive accuracy. Using data of 83,536 GitHub software repositories on cryptocurrency, this article reports the results of experiments that compare the models’ performance in predicting SMN activities over time. Experimental results show that the model (REM) that captures recency/primacy effects of human cognitive processing outperformed other models in 9 (out of 18) measures pertaining to engagement, contribution, influence, and popularity. Primacy plays a dominant role in predicting engagement, contribution, and popularity, whereas recency plays a key role in predicting influence. Short-term trend (modeled with TIM) was found to yield significantly better performance on predicting user contribution. The models also outperformed an integrated machine learning (IML) model by most measures. Overall, the effects modeled by REM and TIM were found to be more significant than the effects modeled by COM, PIM, and IML. The research contributes to enhancing understanding of SMN behavior, developing new models to simulate and predict SMN activities, and designing new artifacts for information systems practitioners to manage knowledge assets and to extract SMN intelligence.

A rough-set and AI based approach for hierarchical cognitive processing of perceptions

Modelling the psychological and cognitive processes is an important issue in theoretical and clinical aspects. Most of the modellings concern much on qualitative and philosophical descriptions. This research resorts to quantitative modelling of human cognitive processing based on rough sets, AI, and mathematical approaches. We formalise psychological targets and properties by objects and features. A feature domain is associated with these objects and features. Based on the subjects’ perception of the values in the feature domain, the cognitive processing is proceeded via deterministic procedures and non-deterministic procedures. The non-deterministic procedures involve the setting of rough set approximations. To evaluate the parameters involved in the setting, we design several experiments and algorithm, in particular probabilistic structures and entropy, to evaluate the effects of these parameters. These provide a great degree of flexibility in practical applications. These approaches could be easily implemented by or combined with machine learning and artificial intelligence techniques. Our approach could be applied directly in clinical assessment and mental health probing.

Representation of an Interdisciplinary Approach for Evaluation of Precision of a Neurocognitive Screening Bundle: A Proposal

Neurocognitive disorders encompass a range of conditions characterized by cognitive impairments, presenting significant challenges to individuals, families, and healthcare systems worldwide. Traditional paper-and-pencil assessments have limitations, including restricted access, limited frequency, and subjective interpretation. This research proposal aims to address these limitations by developing a reliable digital platform, a neurocognitive screening bundle, for year-round monitoring of neurocognitive symptom domains. The proposed research project adopts an interdisciplinary approach by integrating knowledge and expertise from the fields of neurology, psychology, cognitive neuroscience, and computer science. Neurology provides clinical context and expertise in understanding neurocognitive symptom domains, ensuring that the chosen screening bundle aligns with clinical needs. Psychology contributes insights into human behavior and cognitive processes, aiding in the selection, adaptation, or development of assessments targeting specific neurocognitive functions. Additionally, psychologists contribute expertise in data analysis and interpretation, enabling the identification of meaningful patterns within the collected data. Cognitive neuroscience offers a deeper understanding of the underlying neural mechanisms associated with neurocognitive symptom domains. By identifying the neural networks, brain regions, and cognitive processes involved in specific cognitive functions, cognitive neuroscientists bridge the gap between cognitive assessment and neurobiological underpinnings, aiding in the selection and interpretation of neurocognitive measures. Computer science plays a pivotal role in developing and evaluating the digital platform for neurocognitive screening. Computer scientists leverage their expertise in software development, data analysis, and machine learning to design algorithms for data processing, ensure data security and privacy, and develop user-friendly interfaces for assessments. By integrating technical skills with knowledge from other disciplines, computer scientists enhance the accuracy, reliability, and usability of the digital platform. This interdisciplinary research project aims to provide a comprehensive, accessible, and reliable tool for year-round monitoring of neurocognitive symptom domains, surpassing the limitations of traditional assessment methods. By leveraging the expertise from neurology, psychology, cognitive neuroscience, and computer science, this study aims to advance the field and improve clinical outcomes for individuals with neurocognitive disorders. The findings of this research project have the potential to revolutionize neurocognitive assessments, leading to more effective diagnosis and monitoring of these challenging conditions.

A Review on Language Control of Bilingual Speech Production in Language Switching

As bilinguals have increased in number these years, bilingual speech production (BSP) has become a hot topic in psycholinguistic research. The exploration of BSP can not only reveal the psychological mechanism of language processing but also help to understand the general mechanism of information selection in the human cognitive process. Language-switching has currently emerged as one of the primary research paradigms for studying multilingual speech production. Therefore, this paper summarizes several experimental designs of the language-switching paradigm, analyzes observations found in the experiments, summarizes the four theoretical models proposed in the previous literature, and finally puts forward possible future orientations on the basis of current research.

A structural developmental neural network with information saturation for continual unsupervised learning

AbstractIn this paper, we propose a structural developmental neural network to address the plasticity‐stability dilemma, computational inefficiency, and lack of prior knowledge in continual unsupervised learning. This model uses competitive learning rules and dynamic neurons with information saturation to achieve parameter adjustment and adaptive structure development. Dynamic neurons adjust the information saturation after winning the competition and use this parameter to modulate the neuron parameter adjustment and the division timing. By dividing to generate new neurons, the network not only keeps sensitive to novel features but also can subdivide classes learnt repeatedly. The dynamic neurons with information saturation and division mechanism can simulate the long short‐term memory of the human brain, which enables the network to continually learn new samples while maintaining the previous learning results. The parent‐child relationship between neurons arising from neuronal division enables the network to simulate the human cognitive process that gradually refines the perception of objects. By setting the clustering layer parameter, users can choose the desired degree of class subdivision. Experimental results on artificial and real‐world datasets demonstrate that the proposed model is feasible for unsupervised learning tasks in instance increment and class increment scenarios and outperforms prior structural developmental neural networks.

Multimodal Brain Signal Complexity Predicts Human Intelligence.

Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven's Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.

How do medication errors occur in the nursing communication process? Investigating the relationship between error types and error factors.

Human error types and error factors are two important elements of error analysis. Understanding the relationship between them can contribute to new case analyses, the tendency of error occurrence statistics, error factor identification, and prevention of error recurrence. To provide evidence and guidance for the prevention and improvement of medication communication errors by quantitatively exploring the relationship between error types and error factors. Data were collected on self-reported errors in the medication administration process by nurses in all departments of three cooperative medical institutions, and an error sheet of specified style was adopted. Error types were determined by the systematic human error reduction and prediction approach method and human cognition processes. Error factors were extracted using the root cause analysis combined with Berlo's communication model, and the relationship between error types and error factors was quantitatively studied using the partial least-squares regression method. After a one-by-one analysis of 303 error cases, the communication errors occurring in the nursing medication process could be explained by six error types and 12 error factors. In addition, 20 correlation patterns between the error types and error factors were quantitatively obtained, and their path coefficient distributions ranged from 0.088 to 0.467. The results of this study may provide reference to understand errors and establish countermeasures from the statistics of error occurrence trends, extract error factors related to error types and determine key error factors.

EEG Spectral Feature Markers as an Indicator of Human Cognitive Process

Information technologies allow using modern and timely effective analyses of EEG waves and the methods of data processing that allows effective usage of this method into pedagogically and psychologically oriented researches. Aim of this study was to develop and validate method of EEG signal spectral properties usage in the investigations of the process of cognition in the process of the perception of music by the choice of professional studies. 23 research participants took part in the research – the students of the University of Latvia, the division of participants "non-musician" and "musician". The EEG recording synchronized with the musical signal using the generated synchronization signal that given to one of the unipolar input channels of the EEG equipment. The research analyses the basic rhythm of EEG the changes of the maximum frequency and the wave frequency power in the processes connected with the perception and cognition of music for 15 seconds long intervals. During the time of listening to the chorus songs, the range frequency of the range rhythm of alpha and beta does not change to the musicians but during the time of listening to the instrumental music it increases but it was more vivid in the range of beta frequency. Non-musicians reacted differently - while listening to chorus songs and instrumental music the frequency of alfa waves of EEG increased, but the beta wave frequency decreased. EEG as a method of investigation is recommended for pedagogical research to evaluate the neurological functions in the cognitive processes.