Understanding Of Human Behavior Research Articles

Trackintel: An open-source Python library for human mobility analysis

Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data processing pipelines for the varying data collection methods are not standardized and consequently limit the reproducibility, comparability, and transferability of methods and results in quantitative human mobility analysis. This paper presents Trackintel, an open-source Python library for human mobility analysis. Trackintel is built on a standard data model for human mobility used in transport planning that is compatible with different types of tracking data. We introduce the main functionalities of the library that covers the full life-cycle of human mobility analysis, including processing steps according to the conceptual data model, read and write interfaces, as well as analysis functions (e.g., data quality assessment, travel mode prediction, and location labeling). We showcase the effectiveness of the Trackintel library through a case study with four different tracking datasets. Trackintel can serve as an essential tool to standardize mobility data analysis and increase the transparency and comparability of novel research on human mobility. The library is available open-source at https://github.com/mie-lab/trackintel.

Importance of centering traditional knowledge and Indigenous culture in geoscience education

Traditional Knowledge (TK) is a qualitative and quantitative living body of knowledge developed locally and regionally across generations over thousands of years. This study aims to show through authentic voice the importance of centering TK systems and cultural needs to provide equitable geoscience education programs. TK can be communicated through a variety of methods, such as story and song, dance, paintings, carvings, structures, and textiles. TK is interdisciplinary within anthropological and ecological subsistence and provide enhanced cultural and spiritual context. Research findings are enhanced by the exploratory and inquiry-based design of TK and provide insight into the anthropogenic impacts on the environment allowing researchers to gain a rich understanding of human behaviors and patterns when collecting and analyzing data. This study examines factors influencing Indigenous students’ participation and retention in the geosciences, specifically gauging opinions on the incorporation of TK systems into geoscience education. Data was collected using an electronic survey to identify factors that inform students’ decision to enter geoscience disciplines and better understand the importance of role models and mentors for retention. Our findings indicate that Indigenous students were interested in using both TK and Western science in geoscience learning spaces, Indigenous role models played an important role in sense of belonging and identity in the geosciences, and the incorporation of culture into learning experiences played an important role in retention. Findings from this study, if operationalized, would allow geoscience departments to increase retention of Indigenous students and faculty, provide equitable educational opportunities, and to better understand how to effect cultural change in the geosciences by providing a welcoming and affirming space for Indigenous scholars.

Development and Validation of the Sensitivity to Infection Threats Scale (SITS)

The present research emphasizes the role of learning in response changes to infection threats and suggests a new instrument. This preliminary study aims to develop a brief tool (SITS: Sensitivity to Infection Threats Scale) that measures individuals’ health sensitivity to infection threats. The present research utilized the Brief Symptom Inventory—phobic anxiety and hostility subscales and the newly developed SITS. The reliability and validity of SITS were examined through construct, divergent, and convergent validity as well as internal consistency and test-retest reliability. The underlying dimensions were explored through an exploratory factor analysis (EFA N = 142; Mage = 20.29, SDage = 2.34), and the EFA dimensions were confirmed through a confirmatory factor analysis (CFA N = 236; Mage = 20.36, SDage = 2.24). The EFA and CFA results supported a correlated four-factor model and the 20-item structure of the SITS. These four factors included Preoccupied, Avoidant, Physiological, and Cautionary Sensitivities. The overall scale and subscales had good internal consistency, test-retest reliability, and convergent and divergent validity. The SITS is a reliable scale and has the potential to deepen our understanding of human behaviour in responding to infection threats.

Kill chaos with kindness: Agreeableness improves team performance under uncertainty

Teams are central to human accomplishment. Over the past half-century, psychologists have identified the Big-Five cross-culturally valid personality variables: Neuroticism, Extraversion, Openness, Conscientiousness, and Agreeableness. The first four have shown consistent relationships with team performance. Agreeableness (being harmonious, altruistic, humble, and cooperative), however, has demonstrated a non-significant and highly variable relationship with team performance. We resolve this inconsistency through computational modelling. An agent-based model (ABM) is used to predict the effects of personality traits on teamwork, and a genetic algorithm is then used to explore the limits of the ABM in order to discover which traits correlate with best and worst performing teams for a problem with different levels of uncertainty (noise). New dependencies revealed by the exploration are corroborated by analyzing previously unseen data from one of the largest datasets on team performance to date comprising 3698 individuals in 593 teams working on more than 5000 group tasks with and without uncertainty, collected over a 10-year period. Our finding is that the dependency between team performance and Agreeableness is moderated by task uncertainty. Combining evolutionary computation with ABMs in this way provides a new methodology for the scientific investigation of teamwork, making new predictions, and improving our understanding of human behaviors. Our results confirm the potential usefulness of computer modelling for developing theory, as well as shedding light on the future of teams as work environments are becoming increasingly fluid and uncertain.

Теоретический синтез институционального и нарративного подходов в контексте понятия мема

Human rationality has long been an important yet unresolved problem. Narrative and institutional economics, as well as the concept of memes, aim to rationalize the irrational. However, each approach faces challenges when trying to go beyond its traditional field of activity, resulting in fragmentation. The specifi­city of narrative and institutional economics also makes them unsuitable for creating a single, generalized theory of rationalization of the irrational. In contrast, memetics has the potential to produce such a theory. To further this potential, we propose to synthesize the narrative and institutional concepts into a more comprehensive meme concept. Narratives, when interpreted broadly, share characteristics with memes and can be included in the meme concept. Institutions are viewed as tools for disseminating and consolidating memes. Memes are propa­gated through both the verbal and mental activity of people, which comprises not only spoken language but also thought patterns, beliefs, elements of culture, including institutions. This unification offers a potentially productive theoretical framework for the rationalization of the irrational. By understanding how cultural patterns are formed, disseminated, and consolidated, we can gain a better understanding of human behavior and decision-making.

Economic decision-making behavior in the interactive games using brain imaging technology: A short review

Decision-making is an important part of human behavior. With the progress of brain imaging technology in human behavior, a new field called neuroeconomics has emerged recently and attracted wide attention. A large number of studies have used brain imaging technology to study human decision-making behavior. This review emphasized findings obtained in experiments. In this paper, an overview of three popular brain imaging techniques was first provided. Then, this article presents and integrates several typical behavioral and neural experimental studies to further illustrate that the use of brain imaging technology can enhance our understanding of human decision-making behavior. Finally, this paper discusses the challenges at present and puts forward the prospects in neuroeconomics.

Energy flow and functional behavior of individual muscles at different speeds during human walking.

Understanding the distinct functions of human muscles could not only help professionals obtain insights into the underlying mechanisms that we accommodate compromised neuromuscular system, but also assist engineers in developing rehabilitation devices. This study aims to determine the contribution of major muscle and the energy flow in the human musculoskeletal system at four sub-phases (collision, rebound, preload, push-off) during the stance of walking at different speeds. Gait experiments were performed with three self-selected speeds: slow, normal, and fast. Muscle forces and mechanical work were calculated by using a subject-specified musculoskeletal model. The functions of individual muscles were characterized as four functional behaviors (strut, spring, motor, damper), which were determined based on the mechanical energy. The results showed that during collision, hip flexors (iliacus and psoas major) and ankle dorsiflexors (anterior tibialis) were the most dominant muscles in buffering the stride with energy absorption; during rebound, the posterior muscles (gluteus maximus, gastrocnemius, posterior tibialis, soleus) contributed the most to energy generation; during preload, energy for preparing push-off was mainly absorbed by the muscles surrounding knee (vastus, semimembranosus, semitendinosus); during push-off, ankle plantar flexors (gastrocnemius, soleus, posterior tibialis, peroneus muscles, flexor digitorum, flexor hallucis) mainly behaved to generate energy for forward propulsion. With increased walking speed, additional energy (almost 400%) from harder stride was mainly absorbed by the flexor muscles. Hip extensors and adductors transferred more energy (around 150%) to the distal segments during rebound. Soleus and gastrocnemius muscles generated more energy (about 75%) to the proximal segments for propulsion. Along with our previous study of joint-level energy analysis, these findings could assist better understanding of human musculoskeletal behaviors during locomotion and provide principles for the bio-design of related assistive devices from motors performance enhancement to rehabilitation such as exoskeleton and prosthesis.

Relational Frame Theory (RFT): An Empirical Approach to Language and Cognition

The effort of understanding human language and cognition, which distinguishes humans from other organisms and provides them with the opportunities they have on earth, dates back to ancient times. In line with this effort a theory of language and cognition, which is fairly comprehensive and has empirical support, has been developed based on a functional contextualistic view in the last century. This theory, called Relational Frame Theory (RFT), considers language as a verbal behavior and argues that the basis of human language and cognition is the behavior known as Arbitrarily Applicable Relational Responding (AARR). This behavior includes responding based on arbitrary relations independent of the non-arbitrary (physical) relations between stimuli, established by social whim and that do not make sense outside the social context in which they are located. AARR is characterized by three core properties such as mutual entailment, combinatorial mutual entailment and transformation of stimulus functions, and this behavior itself needs to be learned. This review aims to explain RFT, a very complex theory, in a comprehensive and understandable way. The historical process that brought about RFT will be mentioned, the basic principles and concepts of it will be explained, and the contributions of this theory to the understanding of complex human behaviors and its approach to psychopathology will be discussed.

Design science and neuroscience: A systematic review of the emergent field of Design Neurocognition

Design Neurocognition is an emerging research field that aims to elucidate the “black-box” of a designer's mind through the use of brain imaging tools such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS) and magnetic resonance imaging (MRI). More than a decade worth of study has led to many interesting research findings. Here, we systematically review the existing literature in the field. We identify 82 publications and provide an overview of nine main research topics that have been studied. We uncover a large variety of methodological approaches that currently hamper the evaluation of research findings through meta-analysis. Finally, we provide recommendations to advance the field and collaboratively generate a more complete understanding of human behavior during design activities.

Classify Human Activities Based On Deepforest

One important aspect of human behavior understanding is the recognition and monitoring of daily activities. An accurate activity recognition system can improve the quality of life in many key areas. The multi-metric feature extraction and DeepForest classifier designed in this paper effectively had solved the problems of incomprehensive feature extraction and insufficient classifier accuracy.First, this paper extracts a total of 450 dimensional feature vectors using mean, variance, maximum, skewness, minimum, kurtosis, regression of independent variables and sample entropy as feature indicators, so that the features of the original vectors can be presented comprehensively. The dimensionality of the feature set is too large, so PCA is used to reduce the dimensionality of the high-dimensional feature vector. After studying the relationship between the dimensionality reduction and the cumulative contribution rate, it is concluded that the cumulative contribution rate reaches 90% when the dimensionality is 15, which can retain the original features better. Then DeepForest was used as the classifier, and the reduced-dimensional feature set was used as the sample set to divide the training set and test set by 3:1. The model was tested on the test set after training, and the classification accuracy of the test set was 98.202%. Six classifiers, GaussianNB, SVM, K-NN, XGBoost, RandomForest and DecisionTree, were selected as the control experimental group, of which only the RandomForest model reached 97%, while the rest of the control models did not achieve 95% effect, indicating that DeepForest was more accurate in classifying human activities.Next, the generalisation ability of the DeepForest classifier was assessed using Monte Carlo Cross Validation (MCCV), K-fold and its confusion matrix. The MCCV validation used 50% of the data as the training set and 30% of the data as the test set, and set the number of splits to 10, resulting in a mean accuracy of 98.227%. The size of K in the K-fold validation was determined to be 8 based on the number of people conducting the human activity experiment, and the final mean accuracy value obtained was 97.790%. The combined confusion matrix from the K-fold validation (which aggregates the confusion matrix for each classification result) was calculated, and the results showed that the highest accuracy reached 100% for A9 and A14 classifications, where A5 and A10, A15 and A10, A12 and A5 were more likely to be confused, with the highest error rate for A5 classification, which was 4.167%, and the rest of the activity classifications were better.

A few remarks on naturalistic attempts to rationalise hermeneutics

The aim of the text is to consider Gianni Vattimo's claim that hermeneutics needs to be more rational due to its criticised relativism and aestheticism. From this perspective, the author considers the projects proposed by Bartosz Brożek and Chrysostomos Mantzavinos, based on the assumption that the cognitive phenomena underlying the understanding of human behaviour and the resulting artefacts can be described using naturalistic methods. Finally, the question is considered whether these attempts, coming from outside the hermeneutic movement, offer hope for eliminating the flaws of hermeneutics mentioned by Vattimo, and what the prospects are for further research on this issue.

Information literacy, well-being, and rural older adults in a pandemic

Many older adults residing in rural communities faced particularly damaging consequences of the COVID-19 pandemic crisis. Given the impact of the pandemic on the ability to rely on traditional forms of information (i.e. interpersonal communication), reliance on digital technology and other non-traditional sources of information grew. Those who lacked the information literacy, defined in this study as the ability to find and evaluate the reliability of information to address an information need, may have faced psychologically damaging consequences caused by the proliferation of misinformation and information poverty. Informed by an ecological understanding of human information behavior, this paper reports on the findings of a study of rural older adults’ information literacy skills and sense of well-being during the COVID-19 pandemic crisis. A mail survey, based on concepts from psychology and information science and question wordings adapted from the 2021 Health and Retirement Survey and Jones-Jang et al.’s study of literacies and fake news, was distributed in the summer of 2021 to older adults living in rural Kansas. About 206 valid responses to the survey were received. These were analyzed using correlation and regression testing. The findings indicate a significant relationship between information literacy skills and one’s overall sense of well-being. Personal (age, gender, health, life control), economic (employment, finances), and social-relational resources (social engagement, relationship quality) all were shown to relate positively to information literacy skills. These findings provide definition to our understanding of the importance of information literacy in modern understandings of psychological well-being.

Multi-channel network: Constructing efficient GCN baselines for skeleton-based action recognition

Skeleton-based action sequences are widely used for human behaviour understanding due to their compact characteristics. Most existing work designed Graph Convolutional Networks and integrated multiple input channels rather than the original motion sequence to improve the final performance. However, few of them have been reported on the detailed effects of such multiple input channels. In contrast to them, we systemically study the impact of different input channels and construct a more efficient GCN framework. We have identified the complementary effect between the local frame channel and global sequence channel, which is essential to improve the action recognition accuracy. By coupling local frame and global sequence information with a classical spatial–temporal graph neural network, e.g. MS-G3D, it achieves competitive performance compared with SOTA methods on challenging benchmark datasets. Related code would be available at https://github.com/movearbitrarily/multi-stream.

Matching, behavioral economics, and teleological behaviorism: Final cause analysis of substance use and health behavior.

Howard Rachlin and his contemporaries pioneered basic behavioral science innovations that have been usefully applied to advance understanding of human substance use disorder and related health behaviors. We briefly summarize the innovations of molar behaviorism (the matching law), behavioral economics, and teleological behaviorism. Behavioral economics and teleological behaviorism's focus on final causes are especially illuminating for these applied fields. Translational and applied research are summarized for laboratory studies of temporal discounting and economic demand, cohort studies of alcohol and other drug use in the natural environment, and experimental behavioral economic modeling of health behavior-related public health policies. We argue that the teleological behavioral perspective on health behavior is conducive to and merges seamlessly with the contemporary socioecological model of health behavior, which broadens the contextual influences (e.g., community, economic, infrastructure, health care access and policy) of individuals' substance use and other health risk behaviors. Basic-to-applied translations to date have been successful and bode well for continued applications of basic science areas pioneered by Howard Rachlin and his contemporaries.

Control of collective human behavior: Social dynamics beyond modeling

The motion of pedestrians is a paradigmatic phenomenon to study collective human behavior. We propose a model-free approach to analyze the movement of pedestrians in experiments and get a quantitative understanding of crowd dynamics. Using concepts from control and analysis of dynamical systems, we set up a scheme which allows us to identify dynamical unstable signatures in pedestrian flows. These signatures are the building blocks for crowd control and soft management of people and thus result in a fundamental understanding of collective human behavior. Our approach is entirely data driven, and we provide a proof of concept using field and laboratory experiments. In addition, this methodology provides, based on experimental observations, quantitative benchmarks to judge the quality of mathematical models for pedestrian motion.

Data Curation Strategies to Support Responsible Big Social Research and Big Social Data Reuse

Big social research repurposes existing data from online sources such as social media, blogs, or online forums, with a goal of advancing knowledge of human behavior and social phenomena. Big social research also presents an array of challenges that can prevent data sharing and reuse.
 This brief report presents an overview of a larger study that aims to understand the data curation implications of big social research to support use and reuse of big social data. The study, which is based in the United States, identifies six key issues relating to big social research and big social data curation through a review of the literature. It then further investigates perceptions and practices relating to these six key issues through semi-structured interviews with big social researchers and data curators.
 This report concludes with implications for data curation practice: metadata and documentation, connecting with researchers throughout the research process, data repository services, and advocating for community standards. Supporting responsible practices for using big social data can help scale up social science research, thus enhancing our understanding of human behavior and social phenomena.

Development of Grounded Theory in Social Sciences: A Qualitative Approach

Grounded theory (GT) is a general research method that provides the efficient generation of theory from data, which are collected by a strong, sound, and fair research method. It is an inductive methodology that systematically collects and analyzes data for developing theory on human behavior in social welfare perspectives. It is considered as one of the most popular qualitative research methodologies in the world. It is originally developed by two American sociologists Barney Galland Glaser and Anselm Leonard Strauss in 1967 through the publication of their revolutionary book The Discovery of Grounded Theory. It emphasizes the importance of developing an understanding of human behavior through a process of discovery. Grounded theory has originated in sociology, and at present it has become a key methodological setting in a wide range of other disciplines, such as in nursing, physiotherapy, healthcare, education, anthropology, psychology, management, information systems, software engineering, etc. It is useful both for expert and novice researchers to generate new explanatory theories. This article tries to discuss grounded theory methodology through the discussion of its origin and development, basic principles, characteristics, advantages and disadvantages, and its usefulness in social science researches for qualitative analysis.

Graph-based mobility profiling

The decarbonization of the transport system requires a better understanding of human mobility behavior to optimally plan and evaluate sustainable transport options (such as Mobility as a Service). Current analysis frameworks often rely on specific datasets or data-specific assumptions and hence are difficult to generalize to other datasets or studies. In this work, we present a workflow to identify groups of users with similar mobility behavior that appear across several datasets. Our method does not depend on a specific clustering algorithm, is robust against the choice of hyperparameters, does not require specific labels in the dataset, and is not limited to specific types of tracking data. This allows the extraction of stable mobility profiles based on several small and inhomogeneous tracking data sets. Our method consists of the following main steps: Representing individual mobility using location-based graphs; extraction of graph-based mobility features; clustering using different hyperparameter configurations; group identification using statistical testing. The method is applied to six tracking datasets (Geolife, Green Class 1 + 2, yumuv and two Foursquare datasets) with a total of 1070 users that visit about 3′000’000 different locations with a total tracking duration of over 200′000 days. We can identify and interpret five mobility profiles that appear in all datasets and show how these profiles can be used to analyze longitudinal and cross-sectional tracking studies.

Why is it worth studying economics? A course proposal for non-economics students

At an early life stage, students must choose a degree, a crucial decision that will shape their professional life. Whatever the studies chosen, these are very topic specific, and the current higher education system does not have the mechanisms to make this choice less focused, allowing students to open their minds to other areas of knowledge. This situation is particularly critical for students who do not choose a major in a business-related discipline. Basic economic knowledge is paramount as it provides individuals with a better understanding of human behavior and the rules of the marketplace. Knowledge of its foundations is valuable for daily life (e.g. making better choices, allocating resources more effectively, and understanding policy options). However, in some countries, economics is compulsory at neither high school nor university; and thus, it is usually overlooked. Focusing on Spain’s particular context, this study underlines the key reasons for including economics in non-economic university programs. A survey gathering students’ perceptions of the fundamentals of economics and their level of understanding is presented and discussed. Building on the literature and the survey’s results, we identify three core blocks that should be taught. A panel of experts is consulted to evaluate our proposal.

Computational Thinking and User Interfaces: A Systematic Review

Contribution: This document presents a systematic bibliographic review that demonstrates the need to conduct research on how the user experience impacts the development of computational thinking. Background: In the field of computer science, computational thinking is defined as a method that enhances problem-solving skills, system design, and human behavior understanding. Over the last few decades, several tools have been proposed for the development of computational thinking skills; however, there is no area of study that evaluates the implications or the impact that these types of platforms have on users belonging to any knowledge area. Research Question: Do user interfaces influence the development of computational thinking skills? Methodology: To address this issue, a systematic review of the literature was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology for analyzing and evaluating scientific publications. Findings: The results show that despite the dearth of literature on the subject, the specific design of a user interface has a significant impact on the development of computational thinking. Bearing the above in mind, it is necessary to conduct research that delves more deeply into the effects caused by the technologies that are used to develop computational thinking, this being a line of research that is worthy of consideration.