Study Of Human Behavior Research Articles

Ecological brain: reframing the study of human behaviour and cognition.

The last decade has seen substantial advances in the capacity to record behaviour and neural activity in humans in real-world settings, to simulate real-world situations in laboratory settings and to apply sophisticated analyses to large-scale data. Along with these developments, a growing number of groups has begun to advocate for real-world neuroscience and cognitive science. Here, we review the arguments and the available methods for real-world research and outline an overarching framework that embeds key ideas proposed in the literature integrating them into a cyclic process of 'bringing the lab to the real world' (recording behavioural and neural activity in real-world settings) and 'bringing the real-world to the lab' (manipulating the environments in which behaviours occur in the laboratory) that combines exploratory and confirmatory research and is interdisciplinary (including those sciences concerned with the natural, built or virtual environment). We highlight the benefits brought by this framework emphasizing the greater potential for novel discovery, theory development and human-centred applications to the environment.

Exploring Research Dynamics in Aggression and Violence

Background: From 1994 to 2023, the research landscape on the subjects of aggression and violence has grown considerably, influenced by a growth in concerns around mental health, the prevention of violence, and cross-disciplinary approaches in the study of human behavior. The rising complexity of such fields requires a bibliometric analysis for an understanding of the thematic trends, key research areas, and scholarly collaborations that have shaped this body of knowledge. Objective: This study attempts to conduct an inclusive bibliometric analysis representing important themes, research trends, and interdisciplinary relationships of scholarship on psychology, aggression, violence prevention, and other public health-related topics in the past three decades. This paper aims at identifying influential topics, authors, and emerging areas of interest as well as shedding light on potential gaps in this research area. Methods: A bibliometric approach has been applied using VOSviewer for network visualization and mapping. The dataset includes scholarly publications in the period between 1994 and 2023; co-authorship, citation analysis, and keyword co-occurrence are included. Major research clusters, prominent authors, and thematic trends identified in this paper represent the main findings in a visual manner. An overview of key topics and their interrelations is shown visually in word clouds and network visualizations. Findings: Results show that psychology is the most dominant discipline in the discourse of aggression, violence, and public health. Dominant topics include mental health, suicide prevention, poison control, and occupational safety. Other fast-growing topics deal with cyber-aggression and media-fueled violence, reflecting technological changes impinging on behavioral studies. There is considerable interdisciplinary interaction, especially between psychology and medicine and/or public health. These three issues do not cover the broadness of research; other areas, such as environmental health and substance abuse, are underrepresented in the scholarship. Conclusion: This bibliometric analysis underlines the role of psychology and public health as significant in addressing aggression, violence, and mental health. The research landscape is large and interdisciplinary, with future research needing to cover environmental, socio-economic, and systemic factors of public health even more. Enhanced interdisciplinary collaboration and embedding of hitherto less explored topics will play a decisive role in the further development of a comprehensive understanding of the complex issues at play. Novelty: The paper therefore carves out a distinctive bibliometric perspective on the evolution of research in psychology, aggression, and violence within the period 1994-2023, pointing not just toward the themes at the forefront, but also the emergent trends and lacunae in the literature. By identifying key areas of scholarly concentration and underrepresented areas, the analysis constructs a roadmap to future research and advances a more integrated approach toward resolving behavioral and public health problems.

Estimation of human body 3D pose for parent-infant interaction settings using azure Kinect and OpenPose

Automatic pose estimation has become a valuable tool for the study of human behavior, including dyadic interactions. It allows researchers to analyze the nuanced dynamics of interactions more effectively, and facilitates the integration of behavioral data with other modalities (EEG, etc.). However, many technical difficulties remain. Particularly, for parent-infant interactions, automatic pose estimation for infants is unpredictable; the immature proportions and smaller bodies of children may cause misdetections. OpenPose is one tool that has shown high performance in pose tracking from video, even in infants. However, OpenPose is limited to 2D (i.e., coordinates relative to the image space). This may be undesirable in a multitude of paradigms (e.g., naturalistic settings). We developed a method for expanding the functionality of OpenPose to 3D, tailored to parent-infant interaction paradigms. This method merges the estimations from OpenPose with the depth information from a depth camera to obtain a 3D pose that works even for young infants.•Video recordings of interactions of parents and infants are taken using a dual color-depth camera.•2D-positions of parents and their infants are estimated from the color video.•Using the depth camera, we transform the 2D estimations into real-world 3D positions, allowing movement analysis in full-3D space.

Human Behavior Analysis Using Machine Learning

The study and analysis of human behavior have been longstanding challenges in various fields such as psychology, sociology, and anthropology. With the advent of advanced technologies and the proliferation of data, machine learning has emerged as a powerful tool for deciphering and understanding complex human behaviors. This research aims to explore the application of machine learning techniques in the analysis of human behavior, leveraging diverse datasets and cutting-edge algorithms to gain insights into individual and collective actions. The research methodology involves the collection of multidimensional data sources, including but not limited to social media interactions, physiological measurements, and environmental factors. These datasets are preprocessed and integrated to create a comprehensive representation of human behavior. Machine learning models, ranging from traditional statistical methods to deep learning architectures, are then applied to uncover patterns, correlations, and predictive insights from the data. Machine learning algorithms can analyze patterns in behavior to identify potential signs of mental health issues, enabling early intervention and treatment. Tracking behavioral data can help in managing chronic conditions by providing insights into lifestyle choices, adherence to treatment plans, and overall well-being. Machine learning models can identify unusual patterns of behavior, which is crucial for security purposes. This can include recognizing suspicious activities in public spaces or flagging unusual behavior in online environments. The purpose of this study is to explore the challenges of multiple attribute decision-making when dealing with intuitionist fuzzy information. In this scenario, the attribute weights are not entirely known, and the attribute values are represented by intuitionist fuzzy numbers. To determine the attribute weights, an optimization model is constructed based on the traditional grey relational analysis (GRA) fundamental principles. The proposed method involves calculating the grey relation degree between each alternative and the positive-ideal solution and negative-ideal solution. This degree is then used to define a relative relational degree, which enables the ranking of all alternatives simultaneously with respect to both the positive-ideal solution (PIS) and negative-ideal solution (NIS). From the result 22 is ranked at first position and 45 is ranked at fifth position

Civic Education and National Development: A Comprehensive Analysis of Zambia

Overview: Civic education plays a crucial role in the development of nations by fostering informed and responsible citizenship, promoting democratic values, and strengthening social cohesion. Hence, this study presented a thorough examination of the relationship between civic education and national development. The study was conducted in Lusaka district, the capital city of Zambia from which the participants were selected for the study from 5 different organizations. Body of Knowledge: The study comprehended various aspects, including the definition and objectives of civic education, its importance in fostering democratic values, and its impact on social cohesion and economic growth. It dwelt into the crucial role civic education plays in shaping informed and responsible citizens who actively contribute to the progress of their nations. This study was guided by the social learning theory. The social learning theory has been widely applied in the study of human behavior, including in the context of civic education and national development. Methods: The total target population was 1000. The sample size involved a total of 100 respondents, which was 10% of the target population. A mixed method approach was used in this study in combination with the descriptive survey design. Interviews and questionnaires were used to collect data. Tables, graphs and pie-charts were used to analyze the quantitative and qualitative data obtained. Data was then analyzed manually in some cases and also, a combination of software MS Access, MS Excel, and SPSS. Results: The following key findings were established on the effect of civic education on national development; increased awareness, enhanced civic engagement, strengthened community bonds, improved governance understanding, empowered citizenry while regarding the factors that hinder citizen participation in national development, the study found; lack of awareness, limited access to information, distrust in institutions, socioeconomic challenges and ineffective communication channels. Recommendation: The Ministry of Education need to integrating civic education into educational curricula and policymaking initiatives to facilitate sustainable development and democratic governance.

Automatic object detection for behavioural research using YOLOv8.

Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO ('you only look once') and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examines the conditions required for accurate object detection with YOLOv8. The results show almost perfect object detection even when the model was trained on a small dataset (100 to 350 images). The detector, however, does not extrapolate well to the same object in other backgrounds. By training the detector on images from a variety of backgrounds, excellent object detection can be restored. YOLOv8 could be a game changer for behavioural research that requires object annotation in video recordings.

Evaluating Driving Factors of Environmental Behavior of Red Brick Industry Actors During the COVID-19 Pandemic

The number of COVID-19 cases in Indonesia, with a total death toll of 143 445 people as of November 1, 2021, ranked second highest in Asia, disturbing many livelihoods, including the red brick industry. This study aims to analyze the extent to which environmental behavior manifests and assess the capacity and incentive driving forces that affect the environmental behavior of actors in the red brick industry during the COVID-19 pandemic. This research was conducted through questionnaires, data processing, and analysis of descriptive and inferential data using the Statistical Package for the Social Sciences (SPSS). The results showed that the environmental behavior of actors in the red brick industry was less good (12.12%), pretty good (69.69%), and good (18.18%). This study utilized the capacity and incentive variables as driving forces. The variables that contribute to capacity can be categorized as environmental knowledge, social capital, financial capital, natural capital, and physical capital. On the other hand, the incentive variables specifically encompass market signals and government incentives. Neither of the variables have been effective in motivating environmental behavior toward a better or optimal direction. Moreover, their concurrent influence on environmental behavior was weak (8.2%). The study of human behavior focuses on the causal chain. It is, therefore, multi-dimensional, meaning that control or influence of certain factors over behavior is relatively difficult to achieve. The optimal contribution of society toward environmental protection and control has not been realized.

Cube query interestingness: Novelty, relevance, peculiarity and surprise

In this paper, we discuss methods to assess the interestingness of a query in an environment of data cubes. We assume a hierarchical multidimensional database, storing data cubes and level hierarchies. We start with a comprehensive review of related work in the fields of human behavior studies and computer science. We define the interestingness of a query as a vector of scores along different aspects, like novelty, relevance, surprise and peculiarity and complement this definition with a taxonomy of the information that can be used to assess each of these aspects of interestingness. We provide both syntactic (result-independent) and extensional (result-dependent) checks, measures and algorithms for assessing the different aspects of interestingness in a quantitative fashion. We also report our findings from a user study that we conducted, analyzing the significance of each aspect, its evolution over time and the behavior of the study’s participants.

Towards discovering human urban activity—tactics of human spatial behavior

ABSTRACT The study of human spatial behavior primarily focuses on recognizing their spatial diversification and indicating their distribution, which is well described in the literature. In addition to exploring the phenomenon and its description, the essence of human geography is also to explain it. In this context, it is valuable to learn about the practices carried out by users of urban spaces and the tactics they adopt that result in specific spatial behaviors. To date, most research has been concerted on identifying the determinants, needs, and perceptions of space by its users and, in recent years, on psychophysical responses to the surrounding environment. This research investigates how people strive to realize their needs in urban public open spaces. The study was conducted among users of the Liberty Square in Poznań, Poland. Two methods were used during the qualitative field research: observation and individual in-depth interviews of a casual nature. The obtained data were subjected to qualitative analysis using CAQDAS software. The results of the study identified three human spatial behavioral tactics (SBTs) oriented toward (1) space, (2) people, and (3) needs. SBTs mark the course of human activity and determine where it occurs, its duration and intensity. A person chooses one dominant tactic, while the others influence their spatial behavior but play a complementary role. A person determines the proportions between tactics each time, which affects a great deal of flexibility in the course of their spatial behavior.

Understanding Female and Male Insights in Psychology: Who Thinks What?

Evolutionary psychology is the study of human psychological behavior. During childhood, men and women behave similarly; however, as a child approaches puberty, new physical and behavioral changes emerge. Behavioral psychology focuses on understanding the functioning and thought processes of the human mind. The general population lacks knowledge of basic behavioral differences between men and women, leaving them unaware of their role, limitations, societal responsibilities, resulting in an underestimation of their own natural talents and biology. Thus, people tend to follow societal norms rather than exploring and utilizing their natural talents. The current review was designed and conducted to enforce compression on behavioral psychology in both genders as well as to identify variations in hormonal activity and sexual preferences.

Economics as a common interdisciplinary platform–a cybernetic approach

Cybernetics is the study of communication and control processes in biological, mechanical, and electronic systems and as such more relevant to the study of human behavior than simple paradigms, for instance Bounded Rationality (BR). Cybernetic Economics has like other cybernetic systems three levels: Economics aims to maximize the utility of goods and services Relevant Feedback is QALY-effects, for instance: 1) Eco effects, 2) Human relations and 3) Personal income System Management: Ad 1: A carbon neutral economy by a global CO2-emission Tariff (ET) Ad 2: Operation of the Big5 by Neuroeconomic Model (NeM) Ad 1-2: Informal bottom-up support of tripartite management (3P) Ad 3a: Market-based economic growth Ad 3b: Equalization of the market-based income by Universal Basic Income (UBI) Conclusion: A test course indicates that Cybernetic Economics can be disseminated to laymen. The major dissemination challenge is to give specialized researchers an introduction course in Cybernetics. Dissemination of Cybernetic Economics should have top-priority, because it's critical to better democratic guidance of Mankind towards the best possible QALY

Which energy labels should we use to expedite the transition to electric vehicles?

In recent years, numerous countries have enacted legislation to halt fossil-fueled vehicle sales within five to 10 years. With shrinking global markets for these vehicles, manufacturers are increasingly motivated to redirect them towards nations with less stringent regulations, using attractive prices and other tactics. However, the sale of new fossil-fueled vehicles poses a public challenge as they will remain in use for years. Consequently, reducing the likelihood of consumers purchasing new fossil-fueled vehicles in favor of electric vehicles is crucial in all countries, particularly those with less stringent regulations, to meet emission goals. Efforts to promote electric vehicle adoption through policy measures fall short due to underestimated cognitive biases and consumer behavior impacts. We contribute to the literature by bridging the gap between human behavioral studies and environmental policy. We incorporate choice architecture into energy labels to determine which information architecture regarding energy costs is an effective nudge in increasing electric vehicle purchase intentions. Our experiment finds that labels framing energy costs as ‘expenditure,’ rather than ‘savings,’ are more effective in increasing the intent to purchase an electric vehicle. Additionally, we find that a graphical display of expenditure was not effective in influencing purchase intentions. Policymakers can use similar choice architecture tools to encourage electric vehicle adoption, expediting the transition to electric vehicles and achieving national environmental goals.

The Important Role of Dimension in Human Resources Studies: The Curious Case of Employee Satisfaction

Human resources studies are closely related with behavioral study and gained several scholarly attention Indonesia. But there is an important aspect, regarding how scholars use variables in their respective studies, the dimension of a variable is often forgotten. This article will try to shed light on this topic. by doing a thorough review of the case of the employee satisfaction variable. We will explore how the employee satisfaction variable is viewed, and how by looking at the dimension of this variable an entirely new perspective could be found on how this variable interacts with other variables, namely organizational commitment. We will look thoroughly at several studies, especially those that focused on the establishment of the dimension from this variable, and discuss the study's results., Then try to explain, how future research can utilize dimensions to gain a better understanding of human resources and behavioral studies.

Nudging human drivers via implicit communication by automated vehicles: Empirical evidence and computational cognitive modeling

Understanding behavior of human drivers in interactions with automated vehicles (AV) can aid the development of future AVs. Existing investigations of such behavior have predominantly focused on situations in which an AV a priori needs to take action because the human has the right of way. However, future AVs might need to proactively manage interactions even if they have the right of way over humans, e.g., a human driver taking a left turn in front of the approaching AV. Yet it remains unclear how AVs could behave in such interactions and how humans would react to them. To address this issue, here we investigated behavior of human drivers (N = 19) when interacting with an oncoming AV during unprotected left turns in a driving simulator experiment. We measured the outcomes (Go or Stay) and timing of participants’ decisions when interacting with an AV which performed subtle longitudinal nudging maneuvers, e.g. briefly decelerating and then accelerating back to its original speed. We found that participants’ behavior was sensitive to deceleration nudges but not acceleration nudges. We compared the obtained data to predictions of several variants of a drift-diffusion model of human decision making. The most parsimonious model that captured the data hypothesized noisy integration of dynamic information on time-to-arrival and distance to a fixed decision boundary, with an initial accumulation bias towards the Go decision. Our model not only accounts for the observed behavior but can also flexibly generate predictions of human responses to arbitrary longitudinal AV maneuvers, and can be used for both informing future studies of human behavior and incorporating insights from such studies into computational frameworks for AV interaction planning.

A STUDY ON THE IMPACT OF ORGANAIZATIONAL CLIMATE ON EMPLOYEE JOB SATISFACTION WITH SPECIAL REFERENCE TO JOSCO RUBBER PONDY PRIVATET LIMITED

A study of human behaviour is both rewarding and necessary because affair degree of understanding of the people working around him can enable a person to develop his competencies. People need knowledge, attitude and skills to perform tasks. Knowledge can be attained by classroom study and reading. The other factors can be developed by ‘on-the-job’ training. An organization to be successful has to develop the competencies of its employees. Competent and motivated employees are essential for organizational survival, growth and excellence. Motivated employees are the biggest asset of an organization. We live today in an age of rapid changes where liberalization, privatization, globalization, technology movement, quality improvement and the like have become the buzzwords. Competition and survival of the fittest has become the order of the day. In this new order people hold the key. The people-oriented approach focuses on the relevance of human behaviour and innovative management of human resources. Human behaviour has a strong influence on the working environment of an organization. Keywords: Organizational climate, Satisfaction level of employees working

Mental regulation of psychological states in ordinary and stressful conditions

The problem of self-regulation is one of the central ones in the study of human behavior. To maintain sustainable behavior, it is necessary to manage oneself. The mental component of regulation is the least developed in the theoretical constructions of self-regulation of states. In this work, we set a goal – to identify the specifics of the relationships between the components of mental regulation and the characteristics of psychological states in situations with different level of stress. The study sample consisted of 92 people (41 men and 51 women, average age 23.7 years). During the study, standardized methods for diagnosing mental structures (reflexive, semantic, self-system), psychological states, as well as ways of self-regulation were used. Based on the results of the study, we found that in everyday situations, the factor of mental state activity has the greatest weight, i.e. when performing routine tasks, the current state ensures efficiency and adequacy of behavior due to its regulatory function. In a tense situation, the factor of semantic regulation, which includes indicators of life-meaning orientations, comes to the fore. The contribution of mental structures involved in the regulation of the state depends on the characteristics of the situation. Thus, the importance of the components of consciousness in the regulation of mental states is shown. The results of the study can be used in the work of psychologists when planning and conducting activities aimed at increasing the regulatory capabilities of clients, taking into account the degree of tension in the life situation.

Metric information in cognitive maps: Euclidean embedding of non-Euclidean environments.

The structure of the internal representation of surrounding space, the so-called cognitive map, has long been debated. A Euclidean metric map is the most straight-forward hypothesis, but human navigation has been shown to systematically deviate from the Euclidean ground truth. Vector navigation based on non-metric models can better explain the observed behavior, but also discards useful geometric properties such as fast shortcut estimation and cue integration. Here, we propose another alternative, a Euclidean metric map that is systematically distorted to account for the observed behavior. The map is found by embedding the non-metric model, a labeled graph, into 2D Euclidean coordinates. We compared these two models using data from a human behavioral study where participants had to learn and navigate a non-Euclidean maze (i.e., with wormholes) and perform direct shortcuts between different locations. Even though the Euclidean embedding cannot correctly represent the non-Euclidean environment, both models predicted the data equally well. We argue that the embedding naturally arises from integrating the local position information into a metric framework, which makes the model more powerful and robust than the non-metric alternative. It may therefore be a better model for the human cognitive map.

Evaluation of a headphones-fitted EEG system for the recording of auditory evoked potentials and mental workload assessment

Advancements in portable neuroimaging technologies open up new opportunities to gain insight into the neural dynamics and cognitive processes underlying day-to-day behaviors. In this study, we evaluated the relevance of a headphone- mounted electroencephalogram (EEG) system for monitoring mental workload. The participants (N = 12) were instructed to pay attention to auditory alarms presented sporadically while performing the Multi-Attribute Task Battery (MATB) whose difficulty was staged across three conditions to manipulate mental workload. The P300 Event-Related Potentials (ERP) elicited by the presentation of auditory alarms were used as probes of attentional resources available. The amplitude and latency of P300 ERPs were compared across experimental conditions. Our findings indicate that the P300 ERP component can be captured using a headphone-mounted EEG system. Moreover, neural responses to alarm could be used to classify mental workload with high accuracy (over 80%) at a single-trial level. Our analyses indicated that the signal-to-noise ratio acquired by the sponge-based sensors remained stable throughout the recordings. These results highlight the potential of portable neuroimaging technology for the development of neuroassistive applications while underscoring the current limitations and challenges associated with the integration of EEG sensors in everyday-life wearable technologies. Overall, our study contributes to the growing body of research exploring the feasibility and validity of wearable neuroimaging technologies for the study of human cognition and behavior in real-world settings.

Reward conditioning may not have an effect on category-specific memory

Behavioural tagging facilitates the temporary storage of seemingly insignificant episodic events, which may later become salient and enhanced in memory. Human behavioural studies have demonstrated selective memory enhancement for neutral stimuli from one category when this category is subsequently paired with reward. Although this phenomenon has implications for the role of reward conditioning on emotional and adaptive memory, its generalisability is underexplored. We conducted four experiments to investigate whether pairing items from a semantic category, animals or objects, with high or low rewards resulted in preferential memory for the high-reward category. Three of these experiments also aimed to replicate the category-specific retrospective enhancement effect reported by Patil et al. and two explored the corresponding prospective memory effect. None of our experiments showed consistent evidence for an effect of reward on category-specific memory enhancement, despite employing the same reward paradigm and incidental encoding protocol as in the original study. Consequently, we found no evidence for category-specific retrospective or prospective enhancement effects. Our experiments were conducted online which is an equally relevant method for assessing behavioural phenomenon as the in-person studies conducted by Patil et al. Overall, our results question the generalisability of previously reported category-specific memory enhancement effects due to reward.

Unnoticed Child from the Conflict Zone: A Study of Human Behaviour in Select Indian Graphic Novels

Children in conflict-affected regions face numerous obstacles that can substantially impact their cognitive and behavioural development. The purpose of this study is to examine how Indian Graphic Novels, specifically Malik Sajad’s Munnu: A Boy from Kashmir and Varud Gupta & Ayushi Rastogi’s Chhotu: A Tale of Partition and Love, depict the routine of children’s lives in conflict-prone regions. The selected novels effectively convey the lived experiences of the young protagonists and offer an original perspective on their struggles. Children who are deprived of a safe environment due to violence are frequently subjected to financial, sexual, psychological, and physical maltreatment, as well as rules and restrictions from their parents, teachers, and neighbours. The ecological system theory, which contends that a child’s environment affects their general behaviour, is employed in the study. It underlines the effects of living in a conflict zone on behaviour, including post-traumatic stress disorder. Despite the significant influence of parents, culture, and social norms, the study demonstrates that the behaviour of young protagonists living in conflict zones is strongly influenced by their environment (chronosystem). According to the analysis, conflict-related violence against children has become a significant threat to global development. While the number of children affected by the violent environment is unclear, it is evident that those confined to ‘home life’ in a combat zone are at a greater risk of abuse and trauma. The study emphasises the critical requirement for defending the legal rights of children living in conflict areas and giving them a secure environment. It places a strong focus on the value of social services, healthcare, and education in preserving and advancing children’s rights. The purpose of the study is to contribute to a greater understanding of human behaviour in the context of conflict and its potential long-term effects on the development of children. The need to create a just and peaceful world for all children is underlined in the study.