Human Behavior Research Articles

Relationship between sun exposure and seasonal affective disorder symptoms in rural older people with different personalities: a cross-sectional study

ObjectivesEvidence suggests that environmental support, personality traits, and psychological factors can influence seasonal changes in human mood and behavior, particularly in rural middle-aged women and older people. This study aimed to quantify the associations between personality traits, seasonal affective disorder symptoms, and sun exposure in rural older people.MethodsThis study is a cross-sectional analytical study, the participants were 300 rural older persons from 12 natural villages and 5 geriatric service centers in 4 different cities in Jiangxi Province, China. The Eysenck Personality Questionnaire (EPQ), the Personal Inventory of Depression and Seasonal Affective Disorder (PIDS-SA-SimpChi), and the Sunlight Exposure Scale were used to conduct follow-up interviews throughout the year. Spatial analysis was performed using ArcGIS and Geodetic Probes. The data were analyzed using SPSS 21 and Amos 23.0 mediated models.ResultsRural older people with low sun exposure exhibited higher personality trait scores (p < 0.001). Personality traits were directly associated with seasonal affective disorder symptoms(p < 0.01); Sun exposure mediated this effect in rural older people (p < 0.05).ConclusionHigh-scoring personalities are more typical of rural older people with low sun exposure, and there is a greater risk of emotional and behavioral instability. Latitudinal differences are not a determinant of SAD. Increased sun exposure is associated with symptom relief. The promotion of light therapy devices in rural areas with low sunlight is warranted.

Multifunctional Nano‐Conductive Hydrogels With High Mechanical Strength, Toughness and Fatigue Resistance as Self‐Powered Wearable Sensors and Deep Learning‐Assisted Recognition System

AbstractHigh mechanical strength, toughness, and fatigue resistance are essential to improve the reliability of conductive hydrogels for self‐powered sensing. However, achieving mutually exclusive properties simultaneously remains challenging. Hence, a novel directed interlocking strategy based on topological network structure and mechanical training is proposed to construct tough hydrogels by optimizing the network structure and modulating the orientation of molecular chains. Combining Zn2+ crosslinked cellulose nanofibers (CNFs) and a polyacrylamide‐poly(vinyl alcohol) double‐network, the unique interlocked‐network structure exhibits an enhanced toughening effect due to hydrogen bonding and metal‐ligand interactions. The aligned nanocrystalline domains achieved by training further contribute to an increase in the toughness and fatigue thresholds. This innovative approach synergistically enhances the mechanical properties of the nano‐conductive hydrogel, achieving a maximum tensile strength of 4.98 MPa and a toughness of 48 MJ m−3. Notably, the CNFs template with anchored polyaniline, when oriented through mechanical training, forms a unique directional conductive pathway, which significantly enhances the power output performance. Besides, a motion recognition system based on a self‐powered sensing device is designed with the assistance of deep learning techniques to accurately identify human motion behaviors. This work showcases a potentially transformative flexible electronic material for self‐powered sensing systems and intelligent recognition systems.

PSYCHOLOGICAL FOUNDATIONS OF TEACHING LEADERSHIP IN FOREIGN RESOURCES

Teaching leadership skills involves more than just using effective methods and techniques; it also requires having a thorough understanding of the psychological theories that explain human behavior, focusing on key elements like self-awareness, motivation, emotional intelligence, and conflict resolution. The article examines foreign research studies on successful psychological techniques for teaching leadership in higher education. It embraces the importance of developing effective leadership programs, the role of experiential and collaborative learning, development of ethical learning environments and learning opportunities. The significance of a psychologically informed approach to education is highlighted by the necessity for educators to understand the psychological concepts, such as motivation, social dynamics, and cognitive development, that shape instructional practices.

Data simulations for advancing psychological research: Insights, preparations and investigations

Simulation has become an essential tool in psychological research, offering unique insights into statistical concepts, optimising research project planning and modelling human behaviour and cognition. This tutorial navigates simulations' diverse applications across various research stages. This tutorial begins by demonstrating simulations' impact on understanding statistical concepts. Amidst growing demands for effect size reporting and interpretation, simulations can help clarify the relationship between data and effect sizes, benefiting both educational purposes and advanced research. Simulations play a crucial role in planning psychological research. They provide flexible solutions for power, helping to determine necessary sample sizes and aiding in robust research planning, providing evidence of planned procedures. Simulations are pivotal in exploring human behaviour and cognition, as well as statistical method properties. By forcing researchers to translate verbal theory into formal models, simulation can help specify the assumptions. The paper also discusses improving simulation efficiency, such as using calculation clusters, optimising simulation practices and recognises simulations' limitations. This paper emphasises simulations' versatile role in psychological research. They enable deep insights into complex datasets, streamline project preparation and uncover subtleties in human behaviour, cognition and statistical methodologies.

EAV: EEG-Audio-Video Dataset for Emotion Recognition in Conversational Contexts

Understanding emotional states is pivotal for the development of next-generation human-machine interfaces. Human behaviors in social interactions have resulted in psycho-physiological processes influenced by perceptual inputs. Therefore, efforts to comprehend brain functions and human behavior could potentially catalyze the development of AI models with human-like attributes. In this study, we introduce a multimodal emotion dataset comprising data from 30-channel electroencephalography (EEG), audio, and video recordings from 42 participants. Each participant engaged in a cue-based conversation scenario, eliciting five distinct emotions: neutral, anger, happiness, sadness, and calmness. Throughout the experiment, each participant contributed 200 interactions, which encompassed both listening and speaking. This resulted in a cumulative total of 8,400 interactions across all participants. We evaluated the baseline performance of emotion recognition for each modality using established deep neural network (DNN) methods. The Emotion in EEG-Audio-Visual (EAV) dataset represents the first public dataset to incorporate three primary modalities for emotion recognition within a conversational context. We anticipate that this dataset will make significant contributions to the modeling of the human emotional process, encompassing both fundamental neuroscience and machine learning viewpoints.

A Minimal PBPK Model Describes the Differential Disposition of Silica Nanoparticles In Vivo.

Nanoparticles (NPs) have emerged as promising candidates for drug delivery due to their tunable physical and chemical properties. Among these, silica nanoparticles (SiNPs) are particularly valued for their biocompatibility and adaptability in applications like drug delivery and medical imaging. However, predicting SiNP biodistribution and clearance remains a significant challenge. To address this, we developed a minimal physiologically-based pharmacokinetic (mPBPK) model to simulate the systemic disposition of SiNPs, calibrated using in vivo PK data from mice. The model assesses how variations in surface charge, size, porosity, and geometry influence SiNP biodistribution across key organs, including the kidneys, lungs, liver, and spleen. A global sensitivity analysis identified the most influential parameters, with the unbound fraction and elimination rate constants for the kidneys and MPS emerging as critical determinants of SiNP clearance. Non-compartmental analysis (NCA) further revealed that aminated SiNPs exhibit high accumulation in the liver, spleen, and kidneys, while mesoporous SiNPs primarily accumulate in the lungs. Rod-shaped SiNPs showed faster clearance compared to spherical NPs. The mPBPK model was extrapolated to predict SiNP behavior in humans, yielding strong predictive accuracy with Pearson correlation coefficients of 0.98 for mice and 0.92 for humans. This model provides a robust framework for predicting the pharmacokinetics of diverse SiNPs, offering valuable insights for optimizing NP-based drug delivery systems and guiding the translation of these therapies from preclinical models to human applications.

Examining Brain Activity Responses during Rat Ultrasonic Vocalization Playback: Insights from a Novel fMRI Translational Paradigm.

Despite decades of preclinical investigation, there remains limited understanding of the etiology and biological underpinnings of anxiety disorders. Sensitivity to potential threat is characteristic of anxiety-like behavior in humans and rodents, but traditional rodent behavioral tasks aimed to assess threat responsiveness lack translational value, especially with regard to emotionally valenced stimuli. Therefore, development of novel preclinical approaches to serve as analogues to patient assessments is needed. In humans, the fearful face task is widely used to test responsiveness to socially communicated threat signals. In rats, ultrasonic vocalizations (USVs) are analogous social cues associated with positive or negative affective states that can elicit behavioral changes in the receiver. It is therefore likely that when rats hear aversive alarm call USVs (22 kHz), they evoke translatable changes in brain activity comparable with the fearful face task. We used functional magnetic resonance imaging in male and female rats to assess changes in BOLD activity induced by exposure to aversive 22 kHz alarm calls emitted in response to threatening stimuli, prosocial (55 kHz) USVs emitted in response to appetitive stimuli, or a computer-generated 22 kHz tone. Results show patterns of regional activation that are specific to each USV stimulus. Notably, limbic regions clinically relevant to psychiatric disorders (e.g., amygdala, bed nucleus of the stria terminalis) are preferentially activated by either aversive 22 kHz or appetitive 55 kHz USVs. These results support the use of USV playback as a promising translational tool to investigate affective processing under conditions of distal threat in preclinical rat models.

Kulturowe aspekty dark web

The article contains a review of research on the cultural aspects of the Dark Web. A semi-systematic literature review was used for this purpose, as such an approach enables the reviewing of a multi- or interdisciplinary research area. Scopus, an international and multidisciplinary database indexing academic publications, was the source of bibliographic data. It turned out that there is a relatively short history of research on the cultural aspects of the Dark Web. The first publications on the topic only appeared in the Scopus database in 2005, and it continues to be a niche area of research, with the annual number of indexed publications around a few dozen. The author distinguished the following groups of issues that researchers deal with: (1) motivations among Dark Web users for making use of this part of the internet; (2) the impact of the Dark Web’s existence and the anonymity it provides on human behaviour; (3) the specificity of trading activity on “dark markets”; (4) steps taken by illegal traders to gain a reputation and customer trust; (5) communication practices on discussion forums; (6) the freedom to disseminate information; and (7) the methodology for studying communities on “dark networks”.

Behavioural strategies in simultaneous and alternating prisoner’s dilemma games with/without voluntary participation

The Prisoner’s Dilemma is one of the most classic formats for exploring the principle of direct reciprocity. Although numerous theoretical and experimental studies have been conducted, little attention has been paid to the divergence between theoretical predictions and actual human behaviour. In addition, there are two additional essential challenges of experimental research. First, most experimental approaches have focused on games in which two players decide their actions simultaneously, but little is known about alternating games. Another is that there are few experiments on voluntary participation. Here, we conducted experiments on simultaneous games, alternating games, and games with and without voluntary participation for a total of four game patterns and examined the deviation from theoretical predictions for each. The results showed that, contrary to theoretical predictions, humans chose cooperation even after being exploited. We also observed that, with or without voluntary participation, people tended to take the same action they had taken in the previous round. Our results indicate that to understand the mechanisms of human behaviour, we need to integrate findings from behavioural science, psychology, and game theory.

Causal representation learning through higher-level information extraction

The large gap between the generalization level of state-of-the-art machine learning and human learning systems calls for the development of artificial intelligence (AI) models that are truly inspired by human cognition. In tasks related to image analysis, searching for pixel-level regularities has reached a power of information extraction still far from what humans capture with image-based observations. This leads to poor generalization when even small shifts occur at the level of the observations. We explore a perspective on this problem that is directed to learning the generative process with causality-related foundations, using models capable of combining symbolic manipulation, probabilistic reasoning and pattern recognition abilities. We briefly review and explore connections of research from machine learning, cognitive science and related fields of human behavior to support our perspective for the direction to more robust and human-like artificial learning systems.

Human Values in Buddhist Philosophy: A Review

The purpose of this study is to depict human values in Buddhist philosophy. This research is completed in three steps: 1. to present a brief overview on basic human values, 2. to present a brief overview on human values in Buddhist philosophy, 3. the need for human values in today’s society. Buddhist philosophy is a philosophy of human essence. This philosophy teaches people the true human values necessary to become complete human beings. That if held, human life can be shaped and inspired by true human values. And human values are the first condition for building order and just society. Human values refer to relatively stable beliefs composed of a number of attitudes. Human values are the thoughts, goals and objectives that govern and guide human behavior, practices and activities. Fairness, honesty and decency serve as the foundation of human values. Respecting elders, serving others, good manners, tolerance, fairness, honesty etc. are examples of human values. Human values accelerate the mental development of the individual. And by thus developing personality it paves the way for good governance and puts an end to social decay. A child acquires values from family, school, community, playmates, society and traditions. These rules are also the main criteria for the formation of human values. Basically the things that deserve to be human characteristics and qualities are human values. As a result of nurturing these human values, ideological, religious or sacred issues are awakened over time. Through the concept of emotional and ideological unity, human values emerge psychologically in a person, which order and improve the state, society and family. And in this world, the teaching of Buddhist philosophy has strengthened human values among mankind. Therefore, the purpose of this paper is to present basic human values and human values in Buddhist philosophy, the need for human values in today’s society as well. Philosophy and Progress, Vol#73-74; No#1-2; Jan-Dec 2023 P 131-149

PERAN PSIKOLOGI PERKEMBANGAN DALAM DUNIA PENDIDIKAN ANAK SMP

This article discusses the importance of developmental psychology in the education of junior high school children, who are in the age range of 10 to 14 years old. Psychology, derived from the words "psyche" (soul) and "logos" (science), is a field that investigates the mental processes, actions and behaviors of humans in relation to their environment. Developmental psychology specifically studies mental and behavioral changes throughout an individual's life. A deep understanding of developmental psychology is essential for educators and parents of middle school children, in order to understand and respond to their physical, cognitive, social, and emotional developmental needs. The methodology used in this study is the desk research method, which involves collecting and analyzing data from various literature sources such as reference books, articles, and journals. Through this method, the researcher identified important ideas, principles, and perspectives relevant to the topic. The research findings show that developmental psychology plays a vital role in the education of middle school children. Educators need to understand children's developmental phases in order to design appropriate learning programs, as well as identify and address developmental issues that may arise. The main challenges in the developmental process of middle school children include the development of technology and social media, academic pressure, media influence, and mental health issues. The proposed solutions include digital education, study time management, emotional support, monitoring social media use, anti-cyberbullying programs, and access to mental health services. Thus, applying the principles of developmental psychology in the education of junior high school children can create a learning environment that supports physical, emotional, and social development, and produce a quality generation. Collaboration between educators, psychologists and parents is essential to achieve this goal.

Bifurcation Analysis and Optimal Control of an SEIR Model with Limited Medical Resources and Media Impact on Heterogeneous Networks

The spread of epidemic diseases is often considered to be bound up with human behavior. Both treatment and awareness-raising are seen as important preventive measures during an outbreak. In this paper, an SEIR model on heterogeneous network containing saturated treatment function with media information variable is constructed. Through mathematical analysis, the basic reproduction number and equilibriums of the system are derived. The global stability of disease-free equilibrium is further proved based on LaSalle’s invariant principle. It is also found that with a gradual increase of the delayed treatment parameter, the system exhibits a transition from forward bifurcation to backward bifurcation with the variation of the basic reproduction number. Finally, an optimal control problem is formulated with both treatment rate and media information dissemination rate as control variables and solved using the Pontryagin’s maximum principle. All the above results are verified by numerical experiments.

The Impact of Cybersecurity Attacks on Human Trust in Autonomous Vehicle Operations.

This study examines the extent to which cybersecurity attacks on autonomous vehicles (AVs) affect human trust dynamics and driver behavior. Human trust is critical for the adoption and continued use of AVs. A pressing concern in this context is the persistent threat of cyberattacks, which pose a formidable threat to the secure operations of AVs and consequently, human trust. A driving simulator experiment was conducted with 40 participants who were randomly assigned to one of two groups: (1) Experience and Feedback and (2) Experience-Only. All participants experienced three drives: Baseline, Attack, and Post-Attack Drive. The Attack Drive prevented participants from properly operating the vehicle in multiple incidences. Only the "Experience and Feedback" group received a security update in the Post-Attack drive, which was related to the mitigation of the vehicle's vulnerability. Trust and foot positions were recorded for each drive. Findings suggest that attacks on AVs significantly degrade human trust, and remains degraded even after an error-less drive. Providing an update about the mitigation of the vulnerability did not significantly affect trust repair. Trust toward AVs should be analyzed as an emergent and dynamic construct that requires autonomous systems capable of calibrating trust after malicious attacks through appropriate experience and interaction design. The results of this study can be applied when building driver and situation-adaptive AI systems within AVs.

Attitudes Toward Nature as a Key for Understanding the Current Lack of Adequate Environmental Behavior: Overstepping the Dialectic of Extractivism and Romanticism

ABSTRACT This article clarifies the puzzling lack of adequate human environmental behavior, the primary driver of the ongoing climate crisis. It advocates using Wittgensteinian attitude analysis as an investigative framework and argues that attitudes toward nature are crucial yet understudied factors in shaping environmental behavior. The study focuses on the Romantic attitude toward nature as wilderness (understood as the negation of extractivism) and reveals its profound yet often misunderstood adverse impact on environmental behavior. This leads to a reflection on which attitude toward nature could foster more adequate environmental behavior, suggesting kinship as its core.

Reconceptualizing Gender (from the Stream of Life)

Abstract In this paper, we consider a range of trans-inclusive approaches to gender concepts and how they relate to the world, from family resemblance theories to conceptual engineering. In so doing, we shall also examine several concepts that are analogous to gender and other controversial and not-so-controversial concepts, such as “vegan cheese” and “adoptive parents.” Having assessed their merits and demerits, we argue that our gender concepts are not fixed in stone but, rather, evolve alongside human practices and behavior.

Vaccine/Vaccination Hesitancy: Challenging Science and Society

Abstract How disasters (e.g., disease, war, and natural disasters) affect people is reflected in their behavior; in the face of a disaster, people are likely to act out of fear, anxiety, uncertainty, and panic in terms of self-preservation. Following more than 2 years of living amidst a global pandemic, issues of human behavior, and especially individual and societal resilience in response to disasters, are increasingly gaining focus. The theories of planned behavior and cognitive dissonance can help explain behavioral changes and underlying intrinsic conflicts. Especially in relation to global health, such phenomena as discrimination and exclusion are identified as having a negative impact on efforts to contain infectious diseases. In this context, the question arises of how to encourage members of a global society to behave cohesively in terms of quarantine, social distancing, and vaccination. This article demonstrates the existence of disbelief and misrepresentations in the context of vaccine fatigue and skepticism. While vaccinations have helped improve public health, skepticism, and resistance toward their efficacy are increasing. Looking specifically at the relatively new vaccines inoculating against the coronavirus, this article explores factors contributing to vaccine skepticism, especially with regard to Western Europe and the United States.

Detection of aberration in human behavior using shallow neural network over smartphone inertial sensors data

AbstractThe integration of different Mobile Edge Computing (MEC) applications has significantly enhanced the realm of security and surveillance, with Human Activity Recognition (HAR) standing out as a crucial application. The diverse sensors found in smartphones have made it convenient for monitoring applications to gather and analyze data, rendering them valuable for HAR purposes. Moreover, MEC can be employed to automate surveillance, allowing intelligent monitoring of restricted areas to identify and respond to unwanted or suspicious activities. This research develops a system using motion sensors in smartphones to identify unusual human activities. People's smartphones were employed to monitor both suspicious and regular activities. Information was collected for various actions categorized as either suspicious or regular. When a person performs a certain action, the smartphone records a series of sensory data, analyse important patterns from the basic data, and then determines what the person is doing by combining information from different sensors. To prepare the data, information from different sensors was aligned to a shared timeline. In this study, we used a sliding window approach on synchronized data to feed sequences into LSTM and CNN models. These models, which include initial layers of LSTM and CNN, automatically find important patterns in the order of human activities. We combined SVM with the features extracted by the shallow Neural Network to make a mixed model that predicts suspicious activities. Lastly, we compared LSTM, CNN, and our new shallow mixed neural network using a new real‐time dataset. The mixed model of CNN and SVM achieved an accuracy of 94.43%. Additionally, the sliding window method's effectiveness was confirmed with a 4.28% improvement in accuracy.

Integrating pro-environmental behaviors into school-wide positive behavioral interventions and supports for creating green schools

The causes of dramatic climate change mostly lie in human behavior. Humans cause climate change, for example, by creating waste. Pro-environmental behavior plays a crucial role in fighting climate change by preventing waste and conserving resources. Therefore, schools should foster pro-environmental behavior to fight climate change more effectively. Schools can foster pro-environmental behavior by organizing activities focused on both saving water, energy and managing waste through recycling and reuse. As such, one of the systems that can be used to increase the quality and efficiency of green school practices is School-Wide Positive Behavioral Interventions and Supports (SW-PBIS). SW-PBIS is a three-tiered approach that matches intervention intensity to student’s needs, aiming to improve all students’ social and educational outcomes through evidence-based preventive behavioral interventions. However, when SW-PBIS implementations are generally considered, the system is mainly used to prevent problem behaviors. While respect, responsibility, and safety are the most common expectations, no expectation is created or included in the system based on sustainability. So, the starting point of the idea is that while problem behaviors can be prevented with SW-PBIS, pro-environmental behaviors to promote a green school can also be taught and supported. Consequently, this article introduces a conceptual framework to integrate pro-environmental behaviors into SW-PBIS for creating green schools. We discuss the importance of SW-PBIS in fostering pro-environmental behavior and how it can help develop responsible students who contribute to green schools to fight climate change. We also discuss implications for future research and practice. This conceptual framework is expected to expand the scope of SW-PBIS and contribute to the implementation of green school initiatives.

A dopaminergic basis of behavioral control.

Both goal-directed and automatic processes shape human behavior. These processes often conflict, and behavioral control is the decision about which determines behavior. Behavioral control, or deciding how to decide, is critical for adaptive behavior. However, the neural mechanisms underlying behavioral control remain unclear. We performed deep phenotyping of individual dopamine system function by combining PET measures of dopamine physiology, functional MRI, and administration of dopaminergic drugs in a within-subject, double-blind, placebo-controlled design. Subjects performed a rule-based response time task in which we operationalized goal-directed and automatic decision-making as model-based and model-free contributions to behavior, respectively. We found convergent and causal evidence that dopamine D2/3 receptors in the striatum regulate behavioral control by enhancing model-based and blunting model-free influences on behavior. In contrast, we found a double dissociation whereby presynaptic dopamine synthesis capacity in the striatum was linked to acquiring model-based knowledge but not behavioral control. Neuroimaging analysis suggested that striatal D2/3 receptors influence behavioral control by adjusting frontostriatal functional connectivity. This multimodal study establishes a specific role of D2/3 receptors in regulating behavioral control and could contribute to an improved understanding of dysregulated behavioral control in clinical disorders affecting dopamine neurotransmission.