Human Behavior Research Articles

Human Behavior in Social Context

Humans are complex social beings, and their behavior is influenced by interactions with their social environment. In this context, a deep understanding of the factors that shape human behavior is essential. This research aims to understand how cultural values, norms, and traditions influence human behavior in a particular social context. This research uses the case study method. Research subjects may include diverse groups of people involved in social interactions, such as individuals from different age groups, cultures, and socio-economic backgrounds. Data collection techniques include participant observation and case studies. The results show that human behavior in social contexts is strongly influenced by various factors, including culture, norms, and interactions between individuals. Data analysis confirmed that culture plays an important role in shaping behavior, with cultural values influencing individual decisions and actions.

Crossdisciplinary approaches as applied in occupational science

ABSTRACT This discussion paper aimed to explore various cross-disciplinary approaches (i.e., multi-disciplinary, inter-disciplinary, trans-disciplinary, and post-disciplinary) employed in occupational science research. By describing how these approaches are applied in sample studies, our discussion paper shows that taking a cross-disciplinary approach can broaden understandings of complex societal issues related to health, well-being, and human behaviour. Although our paper is data-free and non-systematic, we present our reflections to encourage discussion on the topic of cross-disciplinary approaches. We also want to open the dialogue for occupational scientists to consciously choose cross-disciplinary approaches in their occupational science work. This will enhance the potential for groundbreaking and impactful processes and outcomes both in research and practice.

The views of preschool teachers on the contribution of ICT in Environmental Education

Information and Communication Technologies (ICT) are forms of technology that enhance young children's learning and experiences. When teachers successfully integrate them as targeted tools using computers, tablets, videos, interactive whiteboards, digital games, etc., the quality of teaching and learning can be improved by providing students with an engaging and successful experience. At the same time, environmental conditions worldwide continue to deteriorate at an alarming rate due to a complex web of socio-ecological challenges such as climate change, air pollution, loss of biodiversity, etc. In order to drive transformative changes in human behaviour, it is essential to educate and inform individuals from an early age, starting with kindergarten students. Incorporating new technologies into environmental education and other subjects can spark students' interest and facilitate more effective information dissemination and learning. This study investigated the views of 105 preschool teachers on the contribution of ICT to Environmental Education. The research tool used was a questionnaire distributed to teachers via Google Forms. The research questions pertain to teachers' knowledge and opinions about ICT and EE, as well as the frequency of ICT use and media during teaching interventions. Furthermore, the factors that influence the smooth integration of ICTs into EE teaching and the results that this integration brings were investigated. The research showed that the contribution of ICT in EE positively affected the students. Those that stood out were the active participation, the increased interest, the development of teamwork and the mobilisation of the weak students. However, although the teachers pointed out some difficulties in using ICT in their teaching, such as the deficiency of material and technical infrastructure and the lack of time, they also pointed out how significant and influential the contribution of ICT in teaching Environmental Education is.

Local Contrast Learning for One-Shot Learning

Learning a deep model from small data is an opening and challenging problem. In high-dimensional spaces, few samples only occupy an extremely small portion of the space, often exhibiting sparsity issues. Classifying in this globally sparse sample space poses significant challenges. However, by using a single sample category as a reference object for comparing and recognizing other samples, it is possible to construct a local space. Conducting contrastive learning in this local space can overcome the sparsity issue of a few samples. Based on this insight, we proposed a novel deep learning approach named Local Contrast Learning (LCL). This is analogous to a key insight into human cognitive behavior, where humans identify the objects in a specific context by contrasting them with the objects in that context or from their memory. LCL is used to train a deep model that can contrast the recognized sample with a couple of contrastive samples that are randomly drawn and shuffled. On a one-shot classification task on Omniglot, the deep model-based LCL with 86 layers and 1.94 million parameters, which was trained on a tiny dataset with only 60 classes and 20 samples per class, achieved an accuracy of 98.95%. Furthermore, it achieved an accuracy of 99.24% at 156 classes and 20 samples per class. LCL is a fundamental idea that can be applied to alleviate the parametric model’s overfitting resulting from a lack of training samples.

The time between Palaeolithic hearths.

Resolving the timescale of human activity in the Palaeolithic Age is one of the most challenging problems in prehistoric archaeology. The duration and frequency of hunter-gatherer camps reflect key aspects of social life and human-environment interactions. However, the time dimension of Palaeolithic contexts is generally inaccurately reconstructed because of the limitations of dating techniques1, the impact of disturbing agents on sedimentary deposits2 and the palimpsest effect3,4. Here we report high-resolution time differences between six Middle Palaeolithic hearths from El Salt Unit X (Spain) obtained through archaeomagnetic and archaeostratigraphic analyses. The set of hearths covers at least around 200-240 years with 99% probability, having decade- and century-long intervals between the different hearths. Our results provide a quantitative estimate of the time framework for the human occupation events included in the studied sequence. This is a step forward in Palaeolithic archaeology, a discipline in which human behaviour is usually approached from a temporal scale typical of geological processes, whereas significant change may happen at the smaller scales of human generations. Here we reach a timescale close to a human lifespan.

Climate Change: An Obvious Phenomenon or Myth, in the African Context

Climate change is a global phenomenon of unprecedented consequence, engendering substantial scientific, political, and societal discourse. This literature review article offers an exploration of climate change perceptions and realities in the African context. It begins by establishing the unmistakable scientific consensus, with the Intergovernmental Panel on Climate Change (IPCC) serving as a primary authority. The review highlights robust evidence, including temperature trends, extreme weather events, and ecological impacts across the African continent, affirming climate change as an undeniable scientific phenomenon. However, the discourse surrounding climate change in Africa is not homogeneous. Dissenting arguments challenging climate change as a myth emphasize the variability in public perception, data limitations, and the complexities of attribution. These counterarguments reflect diverse perspectives, economic interests, and political inclinations, contributing to the multifaceted character of the climate change discussion. The implications of these findings are substantial, especially for a region highly susceptible to climate change impacts. Proactive measures for climate mitigation and adaptation are imperative to address the vulnerabilities experienced by communities, ecosystems, and economies. A multidisciplinary approach is underscored, recognizing climate change as an intricate challenge interwoven with economics, politics, and human behaviour. The diversity of voices in this discourse emphasizes the need for ongoing research, collaborative efforts, and an inclusive approach to policy formulation. In navigating the complex terrain of climate change, unwavering commitment to scientific rigor, transparency, and sustainable solutions is paramount. This literature review contributes to the ongoing dialogue, providing a basis for further inquiry and informed decision-making. It has the potential to guide future investigations, shape public policy, and elevate awareness regarding the critical significance of addressing climate change in the African context and, by extension, the entire world. The findings presented here are a testament to the indispensable role of rigorous scholarship in comprehending and confronting one of the most pressing challenges of our time

Simultaneously learning intentions and preferences during physical human-robot cooperation

The advent of collaborative robots allows humans and robots to cooperate in a direct and physical way. While this leads to amazing new opportunities to create novel robotics applications, it is challenging to make the collaboration intuitive for the human. From a system’s perspective, understanding the human intentions seems to be one promising way to get there. However, human behavior exhibits large variations between individuals, such as for instance preferences or physical abilities. This paper presents a novel concept for simultaneously learning a model of the human intentions and preferences incrementally during collaboration with a robot. Starting out with a nominal model, the system acquires collaborative skills step-by-step within only very few trials. The concept is based on a combination of model-based reinforcement learning and inverse reinforcement learning, adapted to fit collaborations in which human and robot think and act independently. We test the method and compare it to two baselines: one that imitates the human and one that uses plain maximum entropy inverse reinforcement learning, both in simulation and in a user study with a Franka Emika Panda robot arm.

Evaluating language models for mathematics through interactions

There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants. However, the standard methodology of evaluating LLMs relies on static pairs of inputs and outputs; this is insufficient for making an informed decision about which LLMs are best to use in an interactive setting, and how that varies by setting. Static assessment therefore limits how we understand language model capabilities. We introduce CheckMate, an adaptable prototype platform for humans to interact with and evaluate LLMs. We conduct a study with CheckMate to evaluate three language models (InstructGPT, ChatGPT, and GPT-4) as assistants in proving undergraduate-level mathematics, with a mixed cohort of participants from undergraduate students to professors of mathematics. We release the resulting interaction and rating dataset, MathConverse. By analyzing MathConverse, we derive a taxonomy of human query behaviors and uncover that despite a generally positive correlation, there are notable instances of divergence between correctness and perceived helpfulness in LLM generations, among other findings. Further, we garner a more granular understanding of GPT-4 mathematical problem-solving through a series of case studies, contributed by experienced mathematicians. We conclude with actionable takeaways for ML practitioners and mathematicians: models that communicate uncertainty, respond well to user corrections, and can provide a concise rationale for their recommendations, may constitute better assistants. Humans should inspect LLM output carefully given their current shortcomings and potential for surprising fallibility.

Studying and improving reasoning in humans and machines

In the present study, we investigate and compare reasoning in large language models (LLMs) and humans, using a selection of cognitive psychology tools traditionally dedicated to the study of (bounded) rationality. We presented to human participants and an array of pretrained LLMs new variants of classical cognitive experiments, and cross-compared their performances. Our results showed that most of the included models presented reasoning errors akin to those frequently ascribed to error-prone, heuristic-based human reasoning. Notwithstanding this superficial similarity, an in-depth comparison between humans and LLMs indicated important differences with human-like reasoning, with models’ limitations disappearing almost entirely in more recent LLMs’ releases. Moreover, we show that while it is possible to devise strategies to induce better performance, humans and machines are not equally responsive to the same prompting schemes. We conclude by discussing the epistemological implications and challenges of comparing human and machine behavior for both artificial intelligence and cognitive psychology.

Polishing methods for composites restoration: the influence on human gingival fibroblasts behaviour

BackgroundCarious/Non-carious cervical lesions with gingival recessions may require both dental and periodontal reconstructive therapy, where flaps/grafts may be placed in contact with a dental filling material. Human Gingival Fibroblasts (HGF-1) response during the early phase of healing could vary according to the procedures employed to cure the dental composite. Moreover, oxygen diffusion into dental composite inhibits the polymerization reaction, creating an oxygen-inhibited layer (OIL) that presents residual unreacted monomers. The aim of this study was to assess the effect of different polishing techniques and OIL on HGF-1.MethodsComposite discs polished with different techniques (diamond rubber, abrasive discs and tungsten carbide burr) were used. An additional not polished smooth group obtained with and without OIL was used as control. Samples were physically characterized through the analysis of their hydrophilicity and surface topography through contact angle measurement and SEM, respectively; afterwards the biologic response of HGF-1 when cultured on the different substrates was analyzed in terms of cytotoxicity and gene expression.ResultsThe finishing systems caused alterations to the wettability, even if without a proportional relation towards the results of the proliferation essay, from which emerges a greater proliferation on surfaces polished with one-step diamond rubber and with abrasive discs as well as a direct effect of the glycerin layer, confirming that surface roughness can heavily influence the biological response of HGF-1.ConclusionsSurfaces wettability as well as cellular behavior seem to be affected by the selection of the finishing system used to lastly shape the restoration. Especially, the presence of OIL act as a negative factor in the regards of human gingival fibroblasts. The present study may provide the first clinical instruction regarding the best polishing system of composite material when the restoration is placed directly in contact with soft tissue cells. Understanding HGF-1 behavior can help identifying the polishing treatment for direct restoration of carious/non-carious cervical lesions associated with gingival recessions.

Impulsivity is a stable, measurable, and predictive psychological trait

Impulsivity is a personality construct frequently employed to explain and predict important human behaviors. Major inconsistencies in its definition and measurement, however, have led some researchers to call for an outright rejection of impulsivity as a psychological construct. We address this highly unsatisfactory state with a large-scale, preregistered study (N = 1,676) in which each participant completed 48 measures of impulsivity derived from 10 self-report scales and 10 behavioral tasks and reported frequencies of seven impulsivity-related behaviors (e.g., impulsive buying and social media usage); a subsample (N = 196) then completed a retest session 3 mo later. We found that correlations between self-report measures were substantially higher than those between behavioral tasks and between self-report measures and behavioral tasks. Bifactor analysis of these measures exacted one general factor of impulsivity I, akin to the general intelligence factor g, and six specific factors. Factor I was related mainly to self-report measures, had high test-retest reliability, and could predict impulsivity-related behaviors better than existing measures. We further developed a scale named the adjustable impulsivity scale (AIMS) to measure I. AIMS possesses excellent psychometric properties that are largely retained in shorter versions and could predict impulsivity-related behaviors equally well as I. These findings collectively support impulsivity as a stable, measurable, and predictive trait, indicating that it may be too early to reject it as a valid and useful psychological construct. The bifactorial structure of impulsivity and AIMS, meanwhile, significantly advance the conceptualization and measurement of construct impulsivity.

Prediction Models of Collaborative Behaviors in Dyadic Interactions: An Application for Inclusive Teamwork Training in Virtual Environments

Collaborative virtual environment (CVE)-based teamwork training offers a promising avenue for inclusive teamwork training. The incorporation of a feedback mechanism within virtual training environments can enhance the training experience by scaffolding learning and promoting active collaboration. However, an effective feedback mechanism requires a robust prediction model of collaborative behaviors. This paper presents a novel approach using hidden Markov models (HMMs) to predict human behavior in collaborative interactions based on multimodal signals collected from a CVE-based teamwork training simulator. The HMM was trained using k-fold cross-validation, achieving an accuracy of 97.77%. The HMM was evaluated against expert-labeled data and compared against a rule-based prediction model, demonstrating the superior predictive capabilities of the HMM, with the HMM achieving 90.59% accuracy compared to 76.53% for the rule-based model. These results highlight the potential of HMMs to predict collaborative behaviors that could be used in a feedback mechanism to enhance teamwork training experiences despite the complexity of these behaviors. This research contributes to advancing inclusive and supportive virtual learning environments, bridging gaps in cross-neurotype collaborations.

Formation of Aesthetic Taste in Students of Professional Educational Organization by Means of Decorative and Applied Art

Introduction. The current stage of development of Russian society is characterized by global socio-economic changes that have caused instability in public life, a crisis of society and its main institutions, the destruction of spiritual ideals, which has led to a decrease in the level of morality and spirituality of young people, the level of formation of aesthetic taste. Therefore, the problem of lack of aesthetic tolerance is relevant, which can lead to negative phenomena in society, indifference or an aggressive attitude towards complex aesthetic phenomena that require intellectual perception. The purpose of the study is to develop and theoretically substantiate a didactic model for the formation of students’ aesthetic taste in an educational environment through the means of arts and crafts and to experimentally test its effectiveness. Methodology. To solve the problems, the following research methods were used: theoretical: ascent from the abstract to the concrete, idealization (creation of an ideal object) as a thought experiment; empirical: observation, pedagogical experiment, study of the results of artistic and creative activities of students. Research instruments are the tools that were used to collect and analyze data. We used the following tools: survey (using the survey we found answers to a number of questions that were developed for a specific group of students); an interview that helped gather information about students’ knowledge of arts and crafts; observation – this tool was used to study human behavior in various situations We studied the level of practical skills of students in the implementation of aesthetic ideas when performing an object of decorative and applied art using the association of a picture, which reflects the national idea, historical symbols and connotations – traditions and rituals, epics in the form of traditional shapes and colors, simple symbols, decorative folklore images. Based on the results obtained, in the course of empirical research, we have developed training programs for in-depth mastery of content in the disciplines “Technology” and “Fine Arts”. The thematic curriculum includes the following sections: 1. Russian art of the 18th–20th centuries. in the works of artists (I. E. Repin, M. P. Botkin, A. G. Venetsianov, P. P. Vereshchagin, etc.). 2. History of Russian culture in epics, fables, poems and fairy tales. 3. Russian decorative art. 4. Ornament as a form of Russian philosophy. 5. Ornament in the works of Khokhloma masters. 6. Ornament of Siberian carpets. 7. Understanding of beauty in Russian painting. 8. The property of Russian literature. Results. As a result of the study, a didactic model for the formation of aesthetic taste of students in the educational environment of a college was developed. The experiment confirmed the effectiveness of the developed curriculum and the identified pedagogical conditions that contribute to the most effective formation of aesthetic taste among students of a professional educational organization through the means of decorative and applied arts. Methodological products were adapted to the conditions of the Chelyabinsk Pedagogical College No. 1. After analyzing the problems identified within the framework of the study, programs for the academic disciplines “Fine Arts and Technology” and methodological recommendations for the formation of artistic and aesthetic taste through the means of decorative and applied arts were developed and implemented into the educational process of designers. Conclusions. The developed curriculum, being part of the educational process at aesthetic faculties, contains specific proposals for improving the training of specialists. Particular attention is paid to tasks for students specializing in painting. This allows us to enrich the experience of implementing creative projects and create aesthetically pleasing products of modern design. The methodological products developed during the study can be used in the professional activities of teachers of vocational training in secondary vocational education organizations.

Open Spaces in a Institutional Campus Leads to Active Environment

Institutional campuses serve as dynamic hubs for learning, collaboration, and community engagement. This paper explores the pivotal role of open spaces within these environments in cultivating activity and vitality. By examining various design elements and their impact on human behaviour, this study elucidates how the strategic integration of open spaces can enhance campus life and promote physical, social, and intellectual engagement. Through case studies and analysis of existing literature, the paper highlights the multifaceted benefits of open spaces, including improved mental well-being, enhanced creativity, and strengthened social connections. Additionally, considerations for inclusive design and sustainable practices are discussed to ensure that open spaces contribute to an equitable and resilient campus environment. Ultimately, this research underscores the significance of thoughtful planning and design interventions in creating vibrant and active institutional campuses that foster holistic development and enrich the overall student experience. Keywords :- Campus ; Open Spaces ; Environment ; Green Space

A Review on User Design Psychology in Human Computer Interaction

In today's world, product design must go beyond function and address consumers' emotional and psychological well-being. This calls for a comprehensive strategy that includes design psychology into the fabric of human-computer interaction interfaces. Understanding human behavior and perception allows designers to develop interfaces that connect with people on a deeper level. The term "Psychology of Design" emphasizes the necessity of studying how human perception effects the design and function of things. It recognizes that design is more than just aesthetics; it is also about how consumers perceive and interact with the product. Integrating psychology into design philosophy represents a paradigm change, acknowledging that good design goes beyond addressing fundamental requirements and involves developing emotional connections. This transition is critical to guaranteeing the lifespan. Keywords— Human perception effects, design psychology.

A behaviour and disease transmission model: incorporating the Health Belief Model for human behaviour into a simple transmission model.

The health and economic impacts of infectious diseases such as COVID-19 affect all levels of a community from the individual to the governing bodies. However, the spread of an infectious disease is intricately linked to the behaviour of the people within a community since crowd behaviour affects individual human behaviour, while human behaviour affects infection spread, and infection spread affects human behaviour. Capturing these feedback loops of behaviour and infection is a well-known challenge in infectious disease modelling. Here, we investigate the interface of behavioural science theory and infectious disease modelling to explore behaviour and disease (BaD) transmission models. Specifically, we incorporate a visible protective behaviour into the susceptible-infectious-recovered-susceptible (SIRS) transmission model using the socio-psychological Health Belief Model to motivate behavioural uptake and abandonment. We characterize the mathematical thresholds for BaD emergence in the BaD SIRS model and the feasible steady states. We also explore, under different infectious disease scenarios, the effects of a fully protective behaviour on long-term disease prevalence in a community, and describe how BaD modelling can investigate non-pharmaceutical interventions that target-specific components of the Health Belief Model. This transdisciplinary BaD modelling approach may reduce the health and economic impacts of future epidemics.

Human Behaviour Detection Dataset (HBDset) Using Computer Vision for Evacuation Safety and Emergency Management

During emergency evacuation, it is crucial to accurately detect and classify different groups of evacuees based on their behaviours using computer vision. Traditional object detection models trained on standard image databases often fail to recognise individuals in specific groups such as the elderly, disabled individuals and pregnant women, who require additional assistance during emergencies. To address this limitation, this study proposes a novel image dataset called the Human Behaviour Detection Dataset (HBDset), specifically collected and annotated for public safety and emergency response purposes. This dataset contains eight types of human behaviour categories, i.e. the normal adult, child, holding a crutch, holding a baby, using a wheelchair, pregnant woman, lugging luggage and using a mobile phone. The dataset comprises more than 1,500 images collected from various public scenarios, with more than 2,900 bounding box annotations. The images were carefully selected, cleaned and subsequently manually annotated using the LabelImg tool. To demonstrate the effectiveness of the dataset, classical object detection algorithms were trained and tested based on the HBDset, and the average detection accuracy exceeds 90%, highlighting the robustness and universality of the dataset. The developed open HBDset has the potential to enhance public safety, provide early disaster warnings and prioritise the needs of vulnerable individuals during emergency evacuation.

User-agnostic adaptation of human locomotion intent: Leveraging Teacher-Student-Learning and ensemble modeling

Wearable robot control in complicated contexts requires an understanding of human locomotion intent and behaviors. However, signals from human–robot interfaces are typically reliant on the user, leading to poor performance of the model trained on training user when applied to new end-point users. This study aims to address this problem by developing a novel Teacher-Student-Learning (TSL) approach using Heterogeneous Ensemble Hypotheses (HEH) to achieve unsupervised user-agnostic adaptation. The motivation behind using the Teacher-Student-Learning architecture is to leverage the diverse features extracted by HEH while maintaining computational efficiency. HEH reduces the difference between labeled training users and unlabeled end-point users by incorporating multiple diverse feature generators to extract a wide range of features, thereby increasing classification accuracy and reducing variance. However, this approach sacrifices efficiency due to the ensemble nature of HEH. To address this trade-off, the knowledge from HEH is distilled into a single network using TSL, ensuring precision and efficiency. The proposed approach is evaluated on two publicly available human locomotion datasets and a 2D moon dataset. Experimental results demonstrate the effectiveness of the proposed method, outperforming all other methods. Tested with three datasets, the proposed method can classify end-point users’ data with an average accuracy of 98.9%, 96.7%, and 96.9% while yielding a low processing time (1 ms). Compared to a benchmark method, the suggested strategy improves the average accuracy by 1.5% and 7.2% for categorizing the target users’ locomotor modes and stabilizes the learning curves. The proposed method represents a significant contribution to the field of human activity recognition and human intent prediction for human–robot interaction systems, enhancing the efficiency and generalization capacity of these systems.

The impact of alcohol on affiliative verbal behavior: Asystematic review and meta-analysis.

Language is a fundamental aspect of human social behavior that is linked to many rewarding social experiences, such as social bonding. Potential effects of alcohol on affiliative language may therefore be an essential feature of alcohol reward and may elucidate pathways through which alcohol is linked to social facilitation. Examinations of alcohol's impact on language content, however, are sparse. Accordingly, this investigation represents the first systematic review and meta-analysis of alcohol's effects on affiliative language. We test the hypothesis that alcohol increases affiliative verbal approach behaviors and discuss future research directions. PsycInfo and Web of Science were systematically searched in March 2023 according to our preregistered plan. Eligible studies included social alcohol administration experiments in which affiliative verbal language was assessed. We present a random-effects meta-analysis that examines the effect of alcohol compared to control on measures of affiliative verbal behavior. Our search identified 16 distinct investigations (comprising 961 participants) that examined the effect of alcohol on affiliative verbal behavior. Studies varied greatly in methods and measures. Meta-analytic results demonstrated that alcohol is modestly associated with increases in affiliative verbal behavior (Hedges' g = 0.164, 95% CI [0.027, 0.301], p = 0.019). Study quality was rated using an adapted version of the Quality Assessment Tool for Quantitative Studies and did not significantly moderate alcohol's effects. This study provides preliminary evidence that alcohol can increase affiliative verbal behaviors. This effect may be an important feature of alcohol reward. Given heterogeneity in study features, low study quality ratings, and limited reporting of effect size data, results simultaneously highlight the promise of this research area and the need for more work. Advances in language processing methodologies that could allow future work to systematically expand upon this finding are discussed.

Human Behavior Learning for a Class of Nonlinear Human-in-the-Loop Systems via Takagi-Sugeno Fuzzy Model

Human Behavior Learning for a Class of Nonlinear Human-in-the-Loop Systems via Takagi-Sugeno Fuzzy Model