Human Behavior Research Articles

It's the biology, stupid! Proxy failures in economic decision making.

Utilitarian characterizations of economic decision making fail to capture the complex, conditional, and heterogeneous motivations underlying human behavior as shaped by the predictive, multicriterial drivers of biological regulation. Unless economic models start to acknowledge that humans have bodies and a biology with its own adaptive logic and tradeoffs, economic policies will be systematically exposed to, and systematic generators of, proxy failures.

A study of the propensity for communicative aggressiveness in cadet pilots

The article presents a model of personal resource of a civil aviation pilot and the experience of studying one of the components of this model – a communicative resource. The article analyses the results of a study of the propensity for communicative aggressiveness of the 5th year cadets of the Ulyanovsk Institute of Civil Aviation named after the Chief Marshal of Aviation B.P. Bugaev who are studying in the field of “Organization of flight work”. 83 people participated in the study. V.V. Bojko’s questionnaire “Determination of integral forms of communicative aggressiveness” was used as a diagnostic tool. This technique is designed to assess a person’s predisposition to aggression in various situations and contains 11 parameters of explicit and hidden forms of aggressive human behaviour. The article substantiates the relevance of studying communicative aggressiveness as a professional destruction of civil aviation pilots. The analysis of the average group and personal values of various parameters characterising the respondents’ propensity to aggressive manifestations was conducted. Recommendations are given on the development of emotional intelligence of future civil aviation pilots in the conditions of their higher professional education. The experience of the Ulyanovsk Institute of Civil Aviation in the prevention and correction of educational and professional stress of participants in the educational process is presented.

BIOLOGY OF HUMAN BEHAVIOR: ITS ESSENTIAL ASPECTS

In terms of Biology humans are just another species on this planet, but when it comes to explaining the biology of the context of Human behavior, it really becomes very tough. Humans have the physiology and anatomy similar to that of a cave man, yet the level of our intelligence has far evolved in comparison to the extent that the cave man anatomy and physiology becomes inadequate to serve to the needs of certain aspects of human intelligence of modern times which essentially functions beyond the level of just survival (for example in animals), and is capable of performing such advanced functions as retaining a huge past memory and can anticipate a future situation. This in many ways has made us far superior to the animal world, yet many of us get messed up in handling our intelligence properly and either develop chronic stress, indulge in violence or mishandle the adolescence related issues of our teenagers. Biologically speaking human behavior is determined largely by how our brains are shaped by developmental and evolutionary processes, at the heart of which lies the genetic and epigenetic changes and environmental impact. Because at any critical moment brains are to decide about any bit of a behavior, therefore the knowledge of underlying brain anatomy and physiology is important. This article is about explaining how biology makes a sense to human behavior and is essentially focused on its three important aspects, i.e. adolescence, chronic stress and violence, which are described in detail in the following paragraphs. KEYWORDS: Amygdala, Brain Reward System, Dopamine, Endocrinology, Homeostasis, Neuropsychology, Prefrontal Cortex. Striatum.

A Study of the Relationship between Human Behavior and Urban Design during the Winter in a High-Snowfall Urban Area

This study focuses on the relationship between the outdoor environment and usage behavior of open spaces in cities with snowy and cold climates (winter cities), using an outdoor survey conducted at AKAPURA Plaza in Sapporo, Japan. This study seeks to understand walking and staying behaviors and analyzes their relationships to snowy outdoor environments. An analysis of the survey data shows that while the number of pedestrians using AKAPLA Plaza decreased as temperatures dropped, the ratio of staying behavior to the number of pedestrians did not decrease despite dropping temperatures. This study identified the following three design principles that can be applied to encourage the usage of open urban spaces during the winter. In winter, the number of pedestrians decreases in correlation with decreases in temperature; however, during the snowy season, walking can be encouraged by providing areas with less snow (PATH). Partially snow-covered areas can encourage photography and snow play behavior (STORAGE). Finally, providing walking routes to snow-covered areas can encourage staying behavior (APPROACH). These design principles were established based on the study in Sapporo and have the potential to be widely applied in other winter cities across the world through future research and analysis.

Potential Misinformation in the Official Disability Guidelines About the Diagnosis and Treatment of Rotator Cuff Tendinopathy.

We selected statements in the Official Disability Guidelines (ODG) that had the potential to reinforce misconceptions regarding symptoms from rotator cuff tendinopathy (RCT). These statements were revised and presented with the original statement to specialists. Twelve statements regarding RCT were identified as deviating from principles based on ethics, values, and the evidence regarding both pathophysiology and human illness behavior. One hundred fifteen upper extremity surgeons reviewed both original and revised versions of the statements and indicated their preference. We found that upper extremity surgeons preferred 3 revised statements, 4 ODG statements, and 5 were rated as neutral between the 2 statements. Statements revised for evidence, ethics, and healthy mindset were not preferred by specialists, which may indicate limited awareness about how negative thoughts and distressing symptoms impact human illness.

Segmentation of the left atrial appendage based on fusion attention.

In clinical practice, the morphology of the left atrial appendage (LAA) plays an important role in the selection of LAA closure devices for LAA closure procedures. The morphology determination is influenced by the segmentation results. The LAA occupies only a small part of the entire 3D medical image, and the segmentation results are more likely to be biased towards the background region, making the segmentation of the LAA challenging. In this paper, we propose a lightweight attention mechanism called fusion attention, which imitates human visual behavior. We process the 3D image of the LAA using a method that involves overview observation followed by detailed observation. In the overview observation stage, the image features are pooled along the three dimensions of length, width, and height. The obtained features from the three dimensions are then separately input into the spatial attention and channel attention modules to learn the regions of interest. In the detailed observation stage, the attention results from the previous stage are fused using element-wise multiplication and combined with the original feature map to enhance feature learning. The fusion attention mechanism was evaluated on a left atrial appendage dataset provided by Liaoning Provincial People's Hospital, resulting in an average Dice coefficient of 0.8855. The results indicate that the fusion attention mechanism achieves better segmentation results on 3D images compared to existing lightweight attention mechanisms.

Academic motivation in adolescents: the role of parental autonomy support, psychological needs satisfaction and self-control

BackgroundVarious perspectives on the existence and degree of motivation, which is a crucial factor influencing human behavior, have been studied for many years. Especially in adolescence, a phase marked by rapid change, motivation plays a crucial role in supporting young people to achieve their goals, fulfill their responsibilities, and experience healthy development.AimsThe present study aims to investigate the structural relationships among perceived parental autonomy support, satisfaction of psychological needs, self-control, and academic motivation.SampleThe study was conducted with a total of 427 high school students, including 230 females and 197 males, aged between 14 and 19 (M = 15.82; SD = 1.02).MethodParticipants completed self-report measures of academic motivation, parental autonomy support, psychological needs satisfaction, and self-control. Structural equation modeling was performed to explore complex relationships between variables of interest.ResultsThe study revealed that psychological needs satisfaction increased as parental autonomy support increased, and self-control also increased with the satisfaction of psychological needs. It was also found that satisfaction of psychological needs was positively related to both intrinsic and extrinsic motivation. Additionally, self-control was associated with increased intrinsic motivation and decreased amotivation. In addition, it was found that parental autonomy support has an indirect relationship with academic motivation through the satisfaction of psychological needs and self-control.ConclusionIt is believed that these connections will lead to a deeper understanding of the significant processes in adolescence and serve as a foundation for developing and implementing psycho-educational interventions related to these variables.

The Power and Roots of Aspirations

Abstract Aspirations have become a common theme in empirical economics studies but there is no unified understanding of the range of outcomes they influence, the factors that shape them, and how to measure them. We survey this growing literature. We argue that there is compelling evidence to consider aspirations as a useful lens to analyze human behavior and development outcomes, at the individual and aggregate levels, in poorer and richer countries. The empirical evidence aligns with the theory that high aspirations can lead individuals to achieve better educational, labor market, and other outcomes and can contribute to making countries more equal and prosperous. The empirical evidence also confirms that the mix of social and circumstantial factors shaping aspirations tends to hinder the aspirations of the disadvantaged—such as the poor, immigrants, and women—and can contribute to vicious circles of poverty, high inequality, low social mobility, and low growth. However, high aspirations should not be considered an end in themselves as they can backfire, with deleterious effects, if unmatched with opportunities. Further, we argue that definitional and measurement issues can affect the understanding of the topic and that studies should more explicitly describe their measures of aspirations to ensure that divergent underlying concepts are not mistaken.

Can Generative AI improve social science?

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative. I examine how bias in the data used to train these tools can negatively impact social science research—as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.

The Uncontrollable Mortality Risk Hypothesis: Theoretical Foundations and Implications for Public Health

Abstract The ‘Uncontrollable Mortality Risk Hypothesis’ employs a behavioural ecological model of human health behaviours to explain the presence of social gradients in health. It states that those who are more likely to die due to factors beyond their control should be less motivated to invest in preventative health behaviours. We outline the theoretical assumptions of the hypothesis and stress the importance of incorporating evolutionary perspectives into public health. We explain how measuring perceived uncontrollable mortality risk can contribute towards understanding socioeconomic disparities in preventative health behaviours. We emphasise the importance of addressing structural inequalities in risk exposure, and argue that public health interventions should consider the relationship between overall levels of mortality risk and health behaviours across domains. We suggest that measuring perceptions of uncontrollable mortality risk can capture the unanticipated health benefits of structural risk interventions, as well as help to assess the appropriateness of different intervention approaches.

DRIVER DROWSINESS DETECTION SYSTEM

The proposed system utilizes facial landmark detection and eye aspect ratio calculation to detect drowsiness in real-time from a webcam feed. It employs the dlib library for face detection and facial landmark prediction. The eye aspect ratio (EAR) is computed from the distances between specific points on the eyes. When the EAR falls below a predefined threshold for a certain number of consecutive frames, it triggers an alert indicating potential drowsiness. The alert includes visual cues on the video feed, an auditory alert using the playsound library, and even sends an instant WhatsApp message using pywhatkit to notify a designated contact about the drowsiness. This system serves as a useful tool for monitoring driver fatigue or alertness in various contexts, promoting safety and awareness. Its capacity to swiftly identify and alert users to the onset of drowsiness represents a significant leap forward in safety technology. By mitigating the risk of accidents stemming from fatigue-induced impairments, this project underscores the pivotal role of computer vision in real-time human behavior monitoring, offering a versatile and invaluable tool for fostering safety and awareness across various domains. Keywords: Drowsiness Detection; Python; Whatsapp messaging; Webcam feed; Computer Vision; Alert System; Real-time monitoring; Eye-Aspect Ratio; Frame Processing; Facial Recognition.

An ontological framework for organising and describing behaviours: The Human Behaviour Ontology

Background Human behaviours have been classified in areas such as health, occupation and sustainability. We aimed to develop a more broadly applicable framework for behaviours to facilitate integrating evidence across domains. Methods The Human Behaviour Ontology (HBO), a part of the Behaviour Change Intervention Ontology (BCIO), was developed by: (1) specifying the ontology’s scope, (2) identifying candidate classes from existing classifications, (3) refining the ontology by applying it to code behaviours in relevant literature, (4) conducting a stakeholder review with behavioural and ontology experts, (5) testing the inter-rater reliability of its use in annotating research reports, (6) finalising classes and adding relations between classes, and (7) publishing the ontology’s computer-readable version. Results A class labelled ‘individual human behaviour’ was defined as “A bodily process of a human that involves co-ordinated contraction of striated muscles controlled by the brain.” In Steps 1-4, the ontology’s initial version was developed, with 128 classes. The inter-rater reliability for applying this version in annotations was 0.63 for researchers familiar with it and, after minor adjustments to the ontology and annotation guidance, 0.74 for researchers unfamiliar with it. Following Steps 5-6, the ontology was published with 177 classes, including 128 individual human behaviour classes organised under upper-level classes relating to (1) experiences (e.g., playing), (2) expressive (e.g., laughing), (3) harm (e.g., self-injury behaviour), (4) health (e.g., undergoing vaccination), (5) life-function (e.g., breathing behaviour), (6) interacting with materials (e.g., consumption), (7) bodily care (e.g., washing), (8) position (e.g., walking), and (9) social environments (e.g., communication). The remaining 49 classes included: ‘individual human behaviour pattern’ for repeated behaviours, ‘population behaviour’, ‘population behaviour pattern’, behavioural attributes (e.g., impulsiveness), and abstinence from behaviour. Relations were also defined to represent timings, locations, participants, mental processes, functions, goals, and outcomes. Conclusions The HBO potentially provides a coherent framework for describing human behaviours.

Identification of maladaptive behavioural patterns in response to extreme weather events

Human behaviour has gained recognition as a critical factor in addressing climate change and its impacts. With extreme weather events posing risks to vulnerable communities, understanding cognitive processes driving behaviours becomes essential for effective risk communication. This study focuses on the 2018 “Vaia” storm, which brought unprecedented precipitation and wind velocity to the mountainous regions of North-eastern Italy. Drawing upon the Protection Motivation Theory (PMT) framework, we employ probabilistic models to identify distinct groups with similar behavioural profiles. By administering a web-based survey to 1500 residents affected by the event, we find that threat appraisal is more influential in shaping protective behaviours than coping appraisal. Our findings indicate that by enhancing coping appraisals and discouraging non-protective measures, we can actively mitigate maladaptive responses and promote the adoption of effective adaptation strategies.

Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home orders

Comprehending the complex interplay among urban mobility, human behavior, and the COVID-19 pandemic could deliver vital perspectives to steer forthcoming public health endeavors. In late 2022, China lifted its "Zero-COVID" policy and rapidly abandoned nearly all interventions. It provides a unique opportunity to observe spontaneous mobility changes without government restriction throughout such a pandemic with high infection. Based on 148 million travel data from the public bus, subway, and taxi systems in Shenzhen, China, our analysis reveals discernible spatial discrepancies within mobility patterns. This phenomenon can be ascribed to the heterogeneous responses of mobility behavior tailored to specific purposes and travel modes in reaction to the pandemic. Considering both the physiological effects of virus infection and subjective willingness to travel, a dynamic model is proposed and capable of fitting fine-grained urban mobility. The analysis and model can interpret mobility data and underlying population behavior to inform policymakers when evaluating public health strategies against future large-scale infectious diseases.

Neural Diversity and Decisions

Abstract Purpose Differences in choices provide a window into the variety of human preferences and behaviors. Most non-trivial decisions recruit multiple regions of the brain with activity that shows substantial variation across individuals and also depends on personality traits and attitudes. Methods The present study measured nine physiologic factors and collected 15 types of psychological data while participants made decisions in standard tasks measuring risk aversion, patience, altruism, cooperation, generosity, trust, and trustworthiness. Robust predictors were defined as those that were statistically significant in both forward and backward stepwise regressions using all the collected independent variables. Results The analysis showed that multiple measures of physiologic stress decrease cooperative behaviors while testosterone consistently increased antisocial behaviors. In addition, cognitive abilities robustly increased patience but decreased cooperation, while those more satisfied with their lives were more trustworthy. Participants scoring high on the personality trait of imagination were shown to be less altruistic and generous, while neuroticism increased prosociality. Conclusion This study identified key drivers of behaviors that should be measured in decision-making experiments in order to capture the multiple factors that affect choices. We conclude with suggestions on how to establish causal relationships between the identified factors and decisions.

Study regarding the influence of a student’s personality and an LMS usage profile on learning performance using machine learning techniques

AbstractAcademic performance (AP) is crucial for lifelong success. Unfortunately, many students fail to meet expected academic benchmarks, leading to altered career paths or university dropouts. This issue is particularly pronounced in the early stages of higher education, highlighting the need for the instructors of these foundational courses to have access to simple yet effective tools for the early identification of students at high risk of academic failure. In this study, we propose a streamlined conceptual model inspired by the Model of Human Behavior (MHB) to which we have incorporated two dimensions: capacity and willingness. These dimensions are assessed through the definition of three variables: Prior Academic Performance (PAP), Personality and Academic Engagement, whose measurements can easily be obtained by the instructors. Furthermore, we outline a Machine Learning (ML) process that higher education instructors can use to create their own tailored models in order to predict AP and identify risk groups with high levels of transparency and interpretability. The application of our approach to a sample of 322 Spanish undergraduates studying two mathematical subjects at a Spanish university demonstrates its potential to detect failure early in the semester with a precision that is comparable with that of more complex models found in literature. Our tailored model identified that capacity was the primary predictor of AP, with a gain-to-baseline improvement of 21%, and the willingness variables increasing this to 27%. This approach is consistent over time. Implications for instructors are discussed and an open prediction and analysis tool is developed.

Adaptive temporal compression for reduction of computational complexity in human behavior recognition

The research on video analytics especially in the area of human behavior recognition has become increasingly popular recently. It is widely applied in virtual reality, video surveillance, and video retrieval. With the advancement of deep learning algorithms and computer hardware, the conventional two-dimensional convolution technique for training video models has been replaced by three-dimensional convolution, which enables the extraction of spatio-temporal features. Specifically, the use of 3D convolution in human behavior recognition has been the subject of growing interest. However, the increased dimensionality has led to challenges such as the dramatic increase in the number of parameters, increased time complexity, and a strong dependence on GPUs for effective spatio-temporal feature extraction. The training speed can be considerably slow without the support of powerful GPU hardware. To address these issues, this study proposes an Adaptive Time Compression (ATC) module. Functioning as an independent component, ATC can be seamlessly integrated into existing architectures and achieves data compression by eliminating redundant frames within video data. The ATC module effectively reduces GPU computing load and time complexity with negligible loss of accuracy, thereby facilitating real-time human behavior recognition.

Closing the loop in minimally supervised human–robot interaction: formative and summative feedback

Human instructors fluidly communicate with hand gestures, head and body movements, and facial expressions, but robots rarely leverage these complementary cues. A minimally supervised social robot with such skills could help people exercise and learn new activities. Thus, we investigated how nonverbal feedback from a humanoid robot affects human behavior. Inspired by the education literature, we evaluated formative feedback (real-time corrections) and summative feedback (post-task scores) for three distinct tasks: positioning in the room, mimicking the robot’s arm pose, and contacting the robot’s hands. Twenty-eight adults completed seventy-five 30-s-long trials with no explicit instructions or experimenter help. Motion-capture data analysis shows that both formative and summative feedback from the robot significantly aided user performance. Additionally, formative feedback improved task understanding. These results show the power of nonverbal cues based on human movement and the utility of viewing feedback through formative and summative lenses.

A Bayesian latent variable model for the optimal identification of disease incidence rates given information constraints

Abstract We present an original approach for measuring infections as a latent variable and making use of serological and expert surveys to provide ground truth identification during the early pandemic period. Compared to existing approaches, our model relies more on empirical information than strong structural forms, permitting inference with relatively few assumptions of cumulative infections. We also incorporate a range of political, economic, and social covariates to richly parameterize the relationship between epidemic spread and human behaviour. To show the utility of the model, we provide robust estimates of total infections that account for biases in COVID-19 cases and tests counts in the U.S. from March to July of 2020, a period of time when accurate data about the nature of the SARS-CoV-2 virus was of limited availability. In addition, we can show how sociopolitical factors like the Black Lives Matter protests and support for President Donald Trump are associated with the spread of the virus via changes in fear of the virus and cell phone mobility. A reproducible version of this article is available as an Rmarkdown file at https://github.com/CoronaNetDataScience/covid_model.

Do Weather Conditions Still Have an Impact on the COVID-19 Pandemic? An Observation of the Mid-2022 COVID-19 Peak in Taiwan

Since the onset of the COVID-19 pandemic in 2019, the role of weather conditions in influencing transmission has been unclear, with results varying across different studies. Given the changes in border policies and the higher vaccination rates compared to earlier conditions, this study aimed to reassess the impact of weather on COVID-19, focusing on local climate effects. We analyzed daily COVID-19 case data and weather factors such as temperature, humidity, wind speed, and a diurnal temperature range from 1 March to 15 August 2022 across six regions in Taiwan. This study found a positive correlation between maximum daily temperature and relative humidity with new COVID-19 cases, whereas wind speed and diurnal temperature range were negatively correlated. Additionally, a significant positive correlation was identified between the unease environmental condition factor (UECF, calculated as RH*Tmax/WS), the kind of Climate Factor Complex (CFC), and confirmed cases. The findings highlight the influence of local weather conditions on COVID-19 transmission, suggesting that such factors can alter environmental comfort and human behavior, thereby affecting disease spread. We also introduced the Fire-Qi Period concept to explain the cyclic climatic variations influencing infectious disease outbreaks globally. This study emphasizes the necessity of considering both local and global climatic effects on infectious diseases.