Models Of Human Behavior Research Articles

Neuromuscular State Estimation Via Space-by-Time Neural Signal Decomposition

Abstract Robotic exoskeletons for the hand are being explored to improve health, safety, and physical performance. However, much research effort is needed to establish reliable models of human behavior for effective human–robot interaction control. In this work, surface electromyography is used to measure and model muscle activity of healthy participants performing quasi-isometric and dynamic hand exercises. Non-negative matrix tri-factorization is used to extract hidden neuromuscular parameters encoded in spatial and temporal muscle synergies, which are used to estimate probabilistic linear models of intent, effort, and fatigue. This paper thereby presents steps toward reliable modeling of nonlinear time-varying hand neuromuscular dynamics for intuitive and robust human–robot interaction.

Ethical Issues of Human Information Behaviour and Human Information Interactions

Abstract The objectives of the study are to determine the main ethical factors related to human information interactions. The main research question was: What is the perception of ethical issues of information work and future topics of information ethics in digital environment? A brief analysis of related ethical issues of information is presented. The main applied methodology was a Delphi study on information ethics with selected experts from Slovakia and Czech Republic. The data were analysed with the use of mixed methods of discourse and content analyses and conceptual modelling. We present results of the discourse analyses (first round, 19 experts; discussion, 6 experts). Results are interpreted with regard to the ethical issues of work with digital information, main ethical dilemmas of the use of advanced technologies, and values of information. Results are visualised in three conceptual maps. A final conceptual model represents the epistemic consensus of experts, including social and cultural rules, value tensions between man and technologies, and utility, truth, and objectivity of information. We propose to include the identified ethical factors into models of human information behaviour. Recommendations for practice focus on value-sensitive design of digital services and products in the intercultural contexts of information literacy, education, science, workplaces, and everyday information use.

Quantifying Entropy in Response Times (RT) Distributions Using the Cumulative Residual Entropy (CRE) Function.

Response times (RT) distributions are routinely used by psychologists and neuroscientists in the assessment and modeling of human behavior and cognition. The statistical properties of RT distributions are valuable in uncovering unobservable psychological mechanisms. A potentially important statistical aspect of RT distributions is their entropy. However, to date, no valid measure of entropy on RT distributions has been developed, mainly because available extensions of discrete entropy measures to continuous distributions were fraught with problems and inconsistencies. The present work takes advantage of the cumulative residual entropy (CRE) function-a well-known differential entropy measure that can circumvent those problems. Applications of the CRE to RT distributions are presented along with concrete examples and simulations. In addition, a novel measure of instantaneous CRE is developed that captures the rate of entropy reduction (or information gain) from a stimulus as a function of processing time. Taken together, the new measures of entropy in RT distributions proposed here allow for stronger statistical inferences, as well as motivated theoretical interpretations of psychological constructs such as mental effort and processing efficiency.

Моделирование процессов социальной самоорганизации интеллектуальных агентов

The relevance of the task of modeling human behavior posed in the article is associated with the need for active development of the economy, ensuring the involvement of an increasing proportion of the young population with higher and secondary specialized education. As a result of state incentives for business, more and more people are ready to receive higher education or continue to study at the next levels. When studying the processes of managing the social behavior of subjects, the “system of systems” (SoS) approach was used, within which various methods of modeling, simulation and analysis are developed and studied. The simulation is based on the multi-agent principle, the mechanism of which allows analyzing the behavior of the self-organization of an intelligent agent (social subject) and, if necessary, changing its behavior. The ideal approach for implementing the modeling of a multi-agent system (MAS) of the behavior of an agent is the evolutionary simulation methodology (ESM), which allows developing a set of interconnected optimization simulation models that implements the property of self-organization of intelligent agents. The basic principles of self-organization control are formulated, which is based on the theory of equilibrium random processes (RSP), which provides the possibility of influencing any available parameters and in any combination of them using ESM. The ways of adapting ESM in relation to modeling the process of social self-organization of a young person are considered, and a graphical interpretation of social development is proposed, two parameters are used as phase coordinates: social status and income level. Models of indirect methods of management, choice of the direction of development of a person’s career growth in the production management system based on dynamic programming have been developed. Examples of ESM implementation are given.

Development of taste perception and complementary foods: A review

Breast milk meets the needs of a 6-month-old child in macronutrients (proteins, fats, carbohydrates) almost entirely, while in minerals and trace elements only partially. Complementary feeding is the stage of development of the child's eating behavior when its diet expands due to the introduction of additional products. The model of human eating behavior is formed in infancy and early childhood when stable taste preferences and attitudes to eating (regimen, serving size, table culture) are established. The European and North American Nutrition Committees, in their resolution (2008), noted that the timing of the introduction of complementary foods for breastfed and bottle-fed infants should be the same: no earlier than week 17 and no later than week 26. The age to introduce complementary foods in Russia is 4-6 months and 5.5 months in breastfed infants. Due to the intensive growth and increased physical activity of the child, it is advisable to use milk porridges as one of the main types of complementary foods necessary for children at 4–5 months to provide an additional (relative to human milk or its substitutes) amount of protein, energy, minerals, and vitamins. In recent years, ready-to-use liquid cereals have become popular. Due to the carbohydrate component of cereals, which promotes longer satiety, they can be given to young children before bedtime.

Pandemic Simulator: An Agent-Based Framework with Human Behavior Modeling for Pandemic-Impact Assessment to Build Sustainable Communities

It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent-based simulator will enable policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM), which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of the PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of COVID-19 are used to validate the proposed model. However, they are highly extendable to all pandemics in general, enabling robust planning for more sustainable communities.

AI-assisted ethics? considerations of AI simulation for the ethical assessment and design of assistive technologies.

Current ethical debates on the use of artificial intelligence (AI) in healthcare treat AI as a product of technology in three ways. First, by assessing risks and potential benefits of currently developed AI-enabled products with ethical checklists; second, by proposing ex ante lists of ethical values seen as relevant for the design and development of assistive technology, and third, by promoting AI technology to use moral reasoning as part of the automation process. The dominance of these three perspectives in the discourse is demonstrated by a brief summary of the literature. Subsequently, we propose a fourth approach to AI, namely, as a methodological tool to assist ethical reflection. We provide a concept of an AI-simulation informed by three separate elements: 1) stochastic human behavior models based on behavioral data for simulating realistic settings, 2) qualitative empirical data on value statements regarding internal policy, and 3) visualization components that aid in understanding the impact of changes in these variables. The potential of this approach is to inform an interdisciplinary field about anticipated ethical challenges or ethical trade-offs in concrete settings and, hence, to spark a re-evaluation of design and implementation plans. This may be particularly useful for applications that deal with extremely complex values and behavior or with limitations on the communication resources of affected persons (e.g., persons with dementia care or for care of persons with cognitive impairment). Simulation does not replace ethical reflection but does allow for detailed, context-sensitive analysis during the design process and prior to implementation. Finally, we discuss the inherently quantitative methods of analysis afforded by stochastic simulations as well as the potential for ethical discussions and how simulations with AI can improve traditional forms of thought experiments and future-oriented technology assessment.

DAMAGE TO THE EDUCATION SECTOR DURING THE WAR. WAYS OF INFLUENCE AND RECONSTRUCTION

Goal. The article discusses the main features of behavioral economics as a new section of economic theory. The new direction in theory is a reflection of the actual change in the model of human behavior, the impact of the digital economy on the individual and the transformation of property relations under the influence of the market environment, which brings to the fore the issues of recognition of rules, the definition of reputation as a level of trust and economic ethics. It is shown that serious shifts in property relations in the behavioral economy act as evidence of the profound nature of the changes in the institution of property rights under the influence of ethical, social, psychological and other behavioral assumptions in economic behavior. Methodology. The study is based on dialectics as a scientific and philosophical method of knowledge, a systematic approach, analytical and expert economic method. First of all, behavioral economics relies on observation and experimentation. A distinctive feature of modern research methods has been the development of an economic model of human behavior based on and influenced by empirical facts collected in the field of behavioral and experimental economics. In essence, the paper uses an interdisciplinary methodology, formed by the efforts of both economists and psychologists, is an example of productive interaction between theoretical and experimental sciences. Results. The article systematically and comprehensively analyzes the specificity of behavioral institutions inherent in the digital economy. It has been revealed that academic economists have come to the realization that not only economic, but also all social institutions, the system of norms and rules, moral values are not only important, but vital for the transformation of all types of property, at the same time the role of the institute of property rights is increasing. It is proved that moral norms are not enough to ensure a climate of trust in the economy. It has been argued that it is especially important to minimize transactional losses by achieving a high level of trust of all agents in the leading economic institutions in conditions of decline in economic and business activity. Scientific novelty. Theoretically proved that behavioral economics is formed as a new intellectual platform of scientific community, a new type of scientific thinking, explicitly taking into account psychological, moral and other behavioral assumptions in economic behavior. It is proved that today the digital economy fundamentally changes the model of the institution of property rights, forms innovative business models, which become an important core of behavioral economics. It is indicated that trust forms the reputation of the subjects and institutions of the behavioral economy. Practical Significance. The practical conclusions of the article are that behavioral economics is a new and promising area of research, which has proposed a completely new model of the individual in the economy and has largely changed the view of modern economic science. Behavioral economics has shown and proved how much human behavior in real life deviates from ideal rational behavior. The reasons for modification of the human model in economics under the influence of various economic, social, psychological and other factors are given. In behavioral economics the problem of formation of new forms of realization of institute of property rights is actual, and any paradigm of behavioral economics should solve the problem of trust in interrelations between economic subjects in preservation of property rights system.

Physics, Biology and Human Faculties: A Structural Stepwise Approach to Modeling Human Behavior

Seeking a better understanding of human behavior and social phenomena were the primary pursuits of Richard Day's scholarship, and of the Journal he co-founded and edited. In that pioneering spirit of adventure, we structure approaches to modeling human behavior in three steps with respect to scientific disciplines, their associated principles, and subject matters: human faculties, biology and physics. We note that the extant practice of modeling human behavior in social sciences starts from psycho-sociological features and principles before moving to biological ones, and finally using physical laws for formalization. These formalizations are often presented with the disclaimer: of course, human behavior extends beyond our physical existence. Alternatively, but less often, they are defended in a reductionist spirit. In the present contribution we propose reversing this extant order of deploying scientific principles and argue that this exercise will help link social sciences to biology and physics, without reductionism.

Route Choice-Based Socio-Technical Macroscopic Traffic Model

Human route choice is undeniably one of the key contributing factors towards traffic dynamics. However, most existing macroscopic traffic models are typically concerned with driving behavior and do not incorporate human route choice behavior models in their formulation. In this paper, we propose a socio-technical macroscopic traffic model that characterizes the traffic states using human route choice attributes. Essentially, such model provides a framework for capturing the Cyber-Physical-Social coupling in smart transportation systems. To derive this model, we first use Cumulative Prospect Theory (CPT) to model the human passengers’ route choice under bounded rationality. These choices are assumed to be influenced by traffic alerts and other incomplete traffic information. Next, we assume that the vehicles are operating under a non-cooperative cruise control scenario. Accordingly, human route choice segregates the traffic into multiple classes where each class corresponds to a specific route between an origin-destination pair. Thereafter, we derive a Mean Field Game (MFG) limit of this non-cooperative game to obtain a macroscopic model which embeds the human route choice attribute. Finally, we analyze the mathematical characteristics of the proposed model, present simulation studies to illustrate the model behavior and show comparison with existing traffic model.

Modeling, Replicating, and Predicting Human Behavior: A Survey

Given the popular presupposition of human reasoning as the standard for learning and decision making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. As such, topics including Game Theory, Theory of Mind, and Machine Learning, among others, integrate concepts that are assumed components of human reasoning. These serve as techniques to replicate and understand the behaviors of humans. In addition, next-generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, allowing them not only to replicate human models as a technique to “learn” but also to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this article is to provide a succinct yet systematic review of important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques that learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without necessarily learning via trial and error.

Modeling of human behavior in norm and mental pathology

Active development of scientific technologies and the digital transformation of the healthcare service promise a serious breakthrough in understanding the norm and pathology, assessing the risks of diseases or specific mental conditions, predicting their course and possible consequences. In other words, there are hopes that in the nearest future science allows modeling and predicting of complex human behavior at least in some contexts. The article analyzes the progress in the field of human behavior modeling in medicine and, particularly, in psychiatry, for which the explanation of behavioral disorders is the most relevant. 1175 publications were reviewed and 74 of them were selected for further analysis of the exiting methods for human behavior modeling in norm and mental pathology. The article highlights the possibilities of human behavior modeling and its most promising prospects. The possibilities of such modeling at the present time are limited. Most part of the created models have no sufficient verification and are unsuitable for solving real practical problems. In addition, science progress is far from explaining complex variants of human behavior, and it is unclear if it is possible to model such behavior using computer architecture, which is significantly different from biological. Various behavioral models are considered, that allow to solve specific practical tasks in psychiatry and healthcare system, and therefore seem to be the most promising.

Special Issue on Human Digital Twin Technology

With the advancement of information technologies such as the Internet of Things and artificial intelligence, cyber-physical systems are being introduced into society. At the core of these systems is the digital twin, a computer model (twin) of a physical entity built in cyberspace for simulation-based prediction. Currently, the digital twin mainly targets artificial objects, such as aircraft engines and factories. However, if it can be extended to humans, it could lead to the realization of human-machine cooperative systems and health promotion services, thereby solving social issues stemming from the aging of society. However, humans are the weakest link in the system, and many technical problems remain to be solved, such as realizing the measurement, modeling, and prediction of human behavior, if the human digital twin is to become reality. This special issue contains 9 papers on developing essential technologies for the human digital twin and constructing human-machine systems for specific applications. The topics covered include learning algorithms, motion measurement and analysis techniques, human perception, system development, and platform software for system development. These clearly show that cross-disciplinary efforts are essential to the realization of the human digital twin. We thank the authors of the papers submitted for this special issue. We are confident that the information provided by the authors is suggestive and informative for both specialists and non-specialists alike. We also sincerely appreciate the efforts of the reviewers. Their contributions helped to make this special issue possible. We hope that this special issue will catalyze sharing across the boundaries of research fields along the path to realizing a human digital twin.

Augmented driver behavior models for high‐fidelity simulation study of crash detection algorithms

AbstractDeveloping safety and efficiency applications for Connected and Automated Vehicles (CAVs) requires a great deal of testing and evaluation. The need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly, possibly dangerous, and time‐consuming. As an alternative, researchers attempt to study and evaluate their algorithms and designs using simulation platforms. Modeling the behavior of drivers or human operators in CAVs or other vehicles interacting with them is one of the main challenges of such simulations. While developing a perfect model for human behavior is a challenging task and an open problem, a significant augmentation of the current models used in simulators for driver behavior is presented. In this paper, a simulation framework for a hybrid transportation system is presented that includes both human‐driven and automated vehicles. In addition, the human driving task is decomposed and a modular approach is offered to simulate a large‐scale traffic scenario, allowing for a thorough investigation of automated and active safety systems. Such representation through Interconnected modules offers a human‐interpretable system that can be tuned to represent different classes of drivers. Additionally, a large driving dataset is analyzed to extract expressive parameters that would best describe different driving characteristics. Finally, a similarly dense traffic scenario is recreated within the simulator and a thorough analysis of various human‐specific and system‐specific factors is conducted, studying their effect on traffic network performance and safety.

Modelling communication-enabled traffic interactions.

A major challenge for autonomous vehicles is handling interactions with human-driven vehicles-for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver's behaviours. We demonstrate our model's potential in a simplified merging scenario of two vehicles, illustrating that it generates plausible interactive behaviour (e.g. aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model's decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.

Influence of personal cooling at local body parts on workers’ thermal comfort levels under thermal environments with elevated ambient temperatures: A model study

Heatwaves, which are defined as prolonged periods of time when the weather is much hotter than usual, have been always serious threats to the occupational health and productivity of workers. To reduce the risk of occupational health hazards during heatwaves, various heat protection methods have been developed and applied. One of the proposed methods is to apply personal cooling onto a certain local body part of the worker, like one's neck or one's torso, using thermoelectric coolers. Several commercial solutions of this type are already available in the market, but a rigorous analysis of the effects of local cooling of body parts is not yet widely available. In this study, we use a human thermal behavior model, i.e., the FIALA-FE model, to assess the effectiveness of such a personal local cooling device under heatwaves. A whole-body thermal comfort index, i.e., predicted mean vote (PMV), and a local thermal sensation index, i.e., Zhang's local thermal comfort index, are calculated under local cooling conditions of various body parts and compared to those without any cooling to assess the effectiveness of such a local cooling strategy. Our result shows that the local cooling strategy may provide only limited protection against extreme thermal conditions like heatwaves, suggesting that basic measures for the protection of workers including assured access to drinking water, frequent rest breaks, reduction of working hours, and suspension of work must remain as the primary means for protecting workers during heatwaves.

Dimension-wise Sequential Update for Learning a Multidimensional Environment in Humans.

When confronted with multidimensional environment problems, humans may need to jointly update multiple state-action-outcome associations across various dimensions. Computational modeling of human behavior and neural activities suggests that such updates are implemented based upon Bayesian update principle. However, it is unclear whether humans perform these updates individually or sequentially. If the update occurs sequentially, the order in which the associations are updated matters and can influence the updated results. To address this question, we tested a few computational models with different update orders using both human behavior and EEG data. Our results indicated that a model undertaking dimension-wise sequential updates was the best fit to human behavior. In this model, ordering the dimensions was decided using entropy, which indexed the uncertainty of associations. Simultaneously collected EEG data revealed evoked potentials that were correlated to the proposed timing of this model. These findings provide new insights into the temporal processes underlying Bayesian update in multidimensional environments.

Affective Expertise

Alongside economic change, market socialism in Vietnam entails biopolitical campaigns to combat poverty as a “social problem.” Social workers in Hồ Chí Minh City function as agents of therapeutic governance to transform the lives of poor urban clients by employing empathetic interpersonal interaction grounded in scientific models of human behavior. Analysis of social workers’ affective expertise illuminates two gendered and classed consequences of their technoscientific interventions. First, social work is feminized, yet social workers often cannot achieve middle-class feminine ideals. Second, the casework approach risks naturalizing class inequality by atomizing structural problems as stemming from individual characteristics that require reform.

Children's Blogging as a Factor of Cultural Transformations

The purpose of the work is to find out the importance of children's blogging for the socialisation and formation of personal qualities of children, to reveal the influence of this phenomenon on the transformation of cultural forms. The research methodology was based on the use of general scientific methods: analysis and synthesis – to develop the source base of the article; as well as specifically scientific, in particular semiotic, helped to find out the mechanism of the emergence of cyber culture, and diachronic-synchronous: children's blogging is considered both in historical development with a cultural interpretation of the results, and linearly – in one historical period of time. Scientific novelty. We have found that currently children's blogging is one of the determining factors in the development of children as individuals and causes the emergence of new forms of culture, in particular, cyber culture. Conclusions. Culture involves the transmission from one generation to the next of historically acquired rules of behaviour, spiritual, and material values for the preservation and development of society. It used to provide direct communication between generations. Nowadays, young people balance between two realities – social and virtual, so they often acquire social and cultural experience primarily on the Internet. Blogs are clearly shaping the preferences of today's children, shaping their cultural self-awareness and defining intercultural competence, and in the future, such a process will acquire even greater dimensions. This led to the emergence of a new generation – bearers of cyber culture, namely, information culture, which as a new cultural model actualises a qualitatively new worldview with corresponding value attitudes, moral and ethical norms, social roles, and models of human behaviour in society.
 Key words: children's blogging, cyber culture, information culture, intercultural competence, social and virtual realities, Internet.

Beyond Active/Passive: Using the Interpersonal Circumplex to Re-Conceptualize Victim Behavior During Violent Crime

ABSTRACT This study used the interpersonal circumplex (IPC) – a probabilistic model of human behavior during social interactions – to conceptualize victim behavior during physically assaultive crime (domestic violence, physical assault, and sexual assault). Using data from the Australian Database of Victimisation Experiences, 101 victim behaviors were identified across a sample of 150 victim narratives. Categorical Principal Components Analysis and Smallest Space Analysis found that victim behavior during physically assaultive crime aligned with the IPC’s four behavioral styles: dominance, submissive, hostility, and cooperation. These findings provide new opportunities to explore victim agency including how victims may influence the offender and situation.