Study Of Human Behavior Research Articles

Explaining human interactions on the road by large-scale integration of computational psychological theory.

When humans share space in road traffic, as drivers or as vulnerable road users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but they need to be captured in models if automated vehicles are to coexist successfully with human road users. Empirical studies of human road user behavior implicate a large number of underlying cognitive mechanisms, which taken together are well beyond the scope of existing computational models. Here, we note that for all of these putative mechanisms, computational theories exist in different subdisciplines of psychology, for more constrained tasks. We demonstrate how these separate theories can be generalized from abstract laboratory paradigms and integrated into a computational framework for modeling human road user interaction, combining Bayesian perception, a theory of mind regarding others' intentions, behavioral game theory, long-term valuation of action alternatives, and evidence accumulation decision-making. We show that a model with these assumptions-but not simpler versions of the same model-can account for a number of previously unexplained phenomena in naturalistic driver-pedestrian road-crossing interactions, and successfully predicts interaction outcomes in an unseen data set. Our modeling results contribute to demonstrating the real-world value of the theories from which we draw, and address calls in psychology for cumulative theory-building, presenting human road use as a suitable setting for work of this nature. Our findings also underscore the formidable complexity of human interaction in road traffic, with strong implications for the requirements to set on development and testing of vehicle automation.

Similarities and Differences between Immersive Virtual Reality, Real World, and Computer Screens: A Systematic Scoping Review in Human Behavior Studies

In the broader field of human behavior studies, there are several trade-offs for on-site experiments. To be tied to a specific location can limit both the availability and diversity of participants. However, current and future technological advances make it possible to replicate real-world scenarios in a virtual environment up to a certain level of detail. How these differences add up and affect the cross-media validity of findings remains a topic of debate. How a virtual world is accessed, through a computer screen or a head-mounted display, may have a significant impact. Not surprisingly, the literature has presented various comparisons. However, while previous research has compared the different devices for a specific research question, a systematic review is lacking. To fill this gap, we conducted this review. We identified 1083 articles in accordance with the PRISMA guidelines. Following screening, 56 articles remained and were compared for a qualitative synthesis to provide the reader a summary of current research on the differences between head-mounted displays (HMDs), computer screens, and the real world. Overall, the data show that virtual worlds presented in an HMD are more similar to real-world situations than to computer screens. This supports the thesis that HMDs are more suitable than computer screens for conducting experiments in the field of human behavioral studies.

Exploring Large Digital Bodies for the Study of Human Behavior.

Internet access has become a fundamental component of contemporary society, with major impacts in many areas that offer opportunities for new research insights. The search and deposition of information in digital media form large sets of data known as digital corpora, which can be used to generate structured data, representing repositories of knowledge and evidence of human culture. This information offers opportunities for scientific investigations that contribute to the understanding of human behavior on a large scale, reaching human populations/individuals that would normally be difficult to access. These tools can help access social and cultural varieties worldwide. In this article, we briefly review the potential of these corpora in the study of human behavior. Therefore, we propose Culturomics of Human Behavior as an approach to understand, explain, and predict human behavior using digital corpora.

The spectacle of knowledge and the Psychology of Education in modernity: under the aegis of the construction of learning

Society gains in its social environment a science that seeks, for plausible reasons, the idea of the human being. The adopted methodology was the bibliographical research. With that comes a new society, where transformation starts to challenge its education. Seeking to understand that current education suffers from a sequence of prejudices since inequality is its greatest challenge, the new education seeks to level these erroneous concepts that are incorporated into human action. Evaluating the system granted to the right and duty of the citizen, whose knowledge results in the psychological development of the being, is that the dynamics of this study will deal in its details with a psychology that transforms, dynamizes and clarifies every reason for being. Since psychology is the study of human behavior, nothing is more striking than inserting into this science the responsibility that should be maintained in the social environment, since moral issues elevate man to the principle that he should respect everyone.

Bounded rationality, enactive problem solving, and the neuroscience of social interaction.

This article aims to show that there is an alternative way to explain human action with respect to the bottlenecks of the psychology of decision making. The empirical study of human behaviour from mid-20th century to date has mainly developed by looking at a normative model of decision making. In particular Subjective Expected Utility (SEU) decision making, which stems from the subjective expected utility theory of Savage (1954) that itself extended the analysis by Von Neumann and Morgenstern (1944). On this view, the cognitive psychology of decision making precisely reflects the conceptual structure of formal decision theory. This article shows that there is an alternative way to understand decision making by recovering Newell and Simon's account of problem solving, developed in the framework of bounded rationality, and inserting it into the more recent research program of embodied cognition. Herbert Simon emphasized the importance of problem solving and differentiated it from decision making, which he considered a phase downstream of the former. Moreover according to Simon the centre of gravity of the rationality of the action lies in the ability to adapt. And the centre of gravity of adaptation is not so much in the internal environment of the actor as in the pragmatic external environment. The behaviour adapts to external purposes and reveals those characteristics of the system that limit its adaptation. According to Simon (1981), in fact, environmental feedback is the most effective factor in modelling human actions in solving a problem. In addition, his notion of problem space signifies the possible situations to be searched in order to find that situation which corresponds to the solution. Using the language of embodied cognition, the notion of problem space is about the possible solutions that are enacted in relation to environmental affordances. The correspondence between action and the solution of a problem conceptually bypasses the analytic phase of the decision and limits the role of symbolic representation. In solving any problem, the search for the solution corresponds to acting in ways that involve recursive feedback processes leading up to the final action. From this point of view, the new term enactive problem solving summarizes this fusion between bounded and embodied cognition. That problem solving involves bounded cognition means that it is through the problem solver's enactive interaction with environmental affordances, and especially social affordances that it is possible to construct the processes required for arriving at a solution. Lastly the concept of enactive problem solving is also able to explain the mechanisms underlying the adaptive heuristics of rational ecology. Its adaptive function is effective both in practical and motor tasks as well as in abstract and symbolic ones.

People are like plutonium

An analogy is drawn between the study of human behavior and the study of plutonium to demonstrate that soft and hard sciences are more similar than different, making the distinction moot and unproductive. The studies of human behavior and plutonium follow a common scientific research cycle that aligns with Thomas Kuhn’s views of scientific change. This common research cycle provides evidence that the thought processes and methodologies required for success are congruent in the soft and hard sciences. The primary implication from this analogy is that scientists in all disciplines should eradicate the distinction between soft and hard sciences. Focusing on similarities rather than differences among researchers from different disciplines is necessary to enhance collective intelligence and the type of transdisciplinary collaboration required to tackle difficult sociotechnical problems.CCS Concepts: • Social and professional topics • User characteristics • Cultural characteristics.

ICatcher+: Robust and Automated Annotation of Infants' and Young Children's Gaze Behavior From Videos Collected in Laboratory, Field, and Online Studies.

Technological advances in psychological research have enabled large-scale studies of human behavior and streamlined pipelines for automatic processing of data. However, studies of infants and children have not fully reaped these benefits because the behaviors of interest, such as gaze duration and direction, still have to be extracted from video through a laborious process of manual annotation, even when these data are collected online. Recent advances in computer vision raise the possibility of automated annotation of these video data. In this article, we built on a system for automatic gaze annotation in young children, iCatcher, by engineering improvements and then training and testing the system (referred to hereafter as iCatcher+) on three data sets with substantial video and participant variability (214 videos collected in U.S. lab and field sites, 143 videos collected in Senegal field sites, and 265 videos collected via webcams in homes; participant age range = 4 months-3.5 years). When trained on each of these data sets, iCatcher+ performed with near human-level accuracy on held-out videos on distinguishing "LEFT" versus "RIGHT" and "ON" versus "OFF" looking behavior across all data sets. This high performance was achieved at the level of individual frames, experimental trials, and study videos; held across participant demographics (e.g., age, race/ethnicity), participant behavior (e.g., movement, head position), and video characteristics (e.g., luminance); and generalized to a fourth, entirely held-out online data set. We close by discussing next steps required to fully automate the life cycle of online infant and child behavioral studies, representing a key step toward enabling robust and high-throughput developmental research.

Evolution of parental roles in phase portraits of bimatrix asymmetric games

In this paper, we address the evolutionary dynamic of parental roles using game theory. The main purpose of the article was to expand a classical list of evolutionary dynamic parental conflicts by adding some important cases which hitherto have not been intensively studied. Our models are apt to deliver some novel insights into the evolution of parental care. We also introduced several hypothetical events that served as illustrations of an arising alteration in cost-benefits for both parents and simulated a subsequent evolutionary endpoint. Our models revealed that evolutionary outcomes for reproductive decisions of both parents could be completely predicted by certain payoff matrices, which serve as proxies for a Darwinian fitness gain. In this sense, the result of a frequency-dependent selection on reproductive traits would inevitably depend on fitness costs and benefits arising for both parents in various circumstances. We demonstrated that population division could be a plausible evolutionary consequence for any human mating game where ‘reproductive defection’ represents the best response to any action by the reproductive opponent. We conclude that future evolutionary studies of human reproductive behavior should be more oriented on estimating a sex-biased asymmetry in potential fitness gains obtained by cooperative and deceptive parents in diverse environments and cultures.

Transdiagnostic Connectome-Based Prediction of Craving.

Craving is a central construct in the study of motivation and human behavior and is also a clinical symptom of substance and non-substance-related addictive disorders. Thus, craving represents a target for transdiagnostic modeling. The authors applied connectome-based predictive modeling (CPM) to functional connectivity data in a large (N=274) transdiagnostic sample of individuals with and without substance use-related conditions, to predict self-reported craving. Functional connectomes derived from three guided imagery conditions of personalized appetitive, stress, and neutral-relaxing experiences were used to predict craving rated before and after each imagery condition. The generalizability of the "craving network" was tested in an independent sample using functional connectomes derived from a cue-induced craving task collected before and after fasting to predict craving rated during fasting. CPM successfully predicted craving, thereby identifying a transdiagnostic "craving network." Anatomical localization of model contribution suggested that the strongest predictors of craving were regions of the salience, subcortical, and default mode networks. As external validation, in an independent sample, the "craving network" predicted food craving during fasting using data from a cue-induced craving task. These data provide a transdiagnostic perspective to a key phenomenological feature of addictive disorders-craving-and identify a common "craving network" across individuals with and without substance use-related disorders, thereby suggesting a neural signature for craving or urge for motivated behaviors.

Human and Environmental Factors Analysis in Traffic Using Agent-Based Simulation

Traffic congestion is a frequent problem on most urban roads. This may be due to incorrect configuration of traffic signals but planning analysis should also include a study of human behavior, which, often imprudent, contributes to traffic congestion. The aim of this paper is to analyze the influence of human factors and their reaction to the environment on the impact of traffic performance through waiting time. For this purpose, an agent-based simulation is developed to represent the autonomous and social behavior of road users. The waiting of vehicles at signals is modeled on the basis of a queuing system. Simulations and experiments are based on the analysis of the age of the people and the condition of the pavement. Results show that people’s age is the most important factor influencing their behavior on the road. It is also shown external factors that also affect driver response and thus signal impact, such as the condition of the pavement. Finally, traffic performance, measured by waiting time, depends strongly on the behavior of people facing signals, according to their characteristics and factors present in the environment.

Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology

In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants' self-evaluation. Affective computing uses biometrics to recognize people's emotional states as they interact with a product. However, the cost of medical-grade biofeedback systems is prohibitive for researchers with limited budgets. An alternative solution is to use consumer-grade devices, which are more affordable. However, these devices require proprietary software to collect data, complicating data processing, synchronization, and integration. Additionally, researchers need multiple computers to control the biofeedback system, increasing equipment costs and complexity. To address these challenges, we developed a low-cost biofeedback platform using inexpensive hardware and open-source libraries. Our software can serve as a system development kit for future studies. We conducted a simple experiment with one participant to validate the platform's effectiveness, using one baseline and two tasks that elicited distinct responses. Our low-cost biofeedback platform provides a reference architecture for researchers with limited budgets who wish to incorporate biometrics into their studies. This platform can be used to develop affective computing models in various domains, including ergonomics, human factors engineering, user experience, human behavioral studies, and human-robot interaction.

Effects of the Uncertainty of Interpersonal Communications on Behavioral Responses of the Participants in an Immersive Virtual Reality Experience: A Usability Study.

Two common difficulties which people face in their daily lives are managing effective communication with others and dealing with what makes them feel uncertain. Past research highlights that the result of not being able to handle these difficulties influences people's performance in the task at hand substantially, especially in the context of a social environment such as a workplace. Perceived uncertainty of information is a key influential factor in this regard, with effects on the quality of the information transfer between sender and receiver. Uncertainty of information can be induced into the communication system in three ways: when there is any kind of information deficit that makes the target message unclear for the receiver, when there are some requested changes that could not be predicted by the receiver, and when the content of the message is so interconnected and complex that it limits understanding. Since uncertainty is an inseparable feature of our lives, studying the effects that different levels of it have on individuals and how individuals nevertheless accomplish the tasks of daily living is of high importance. Modern technologies such as immersive virtual reality (VR) have been successful in providing effective platforms to support human behavioral and social well-being studies. In this paper, we suggest the design, development, and evaluation of an immersive VR serious game platform to study behavioral responses to the uncertain features of interpersonal communications. In addition, we report the result of a within-subject user study with 17 participants aged between 20 and 35 and their behavioral responses to two levels of uncertainty with subjective and objective measures. The results convey that the application successfully and meaningfully measured some behavioral responses related to exposure to different levels of uncertainty and overall, the participants were satisfied with the experience.

Computer anthropomorphisation in a socio-economic dilemma.

In the study of human behaviour, non-social targets are often used as a control for human-to-human interactions. However, the concept of anthropomorphisation suggests that human-like qualities can be attributed to non-human objects. This can prove problematic in psychological experiments, as computers are often used as non-social targets. Here, we assessed the degree of computer anthropomorphisation in a sequential and iterated prisoner's dilemma. Participants (N = 41) faced three opponents in the prisoner's dilemma paradigm-a human, a computer, and a roulette-all represented by images presented at the commencement of each round. Cooperation choice frequencies and transition probabilities were estimated within subjects, in rounds against each opponent. We found that participants anthropomorphised the computer opponent to a high degree, while the same was not found for the roulette (i.e. no cooperation choice difference vs human opponents; p = .99). The difference in participants' behaviour towards the computer vs the roulette was further potentiated by the precedent roulette round, in terms of both cooperation choice (61%, p = .007) and cooperation probability after reciprocated defection (79%, p = .007). This suggests that there could be a considerable anthropomorphisation bias towards computer opponents in social games, even for those without a human-like appearance. Conversely, a roulette may be a preferable non-social control when the opponent's abilities are not explicit or familiar.

Ramsey and Keynes revisited

Abstract This paper re-assesses Ramsey’s influence on Keynes. It is argued that the Standard View has restricted attention to the implications for probability theory of Ramsey’s criticisms of Keynes’s concepts of logical probability-relations and non-numerical probabilities. Building on the work of both Coates (1996) and Misak (2016), an Alternative View is proposed in which Ramsey’s influence on Keynes is seen as principally philosophical. Specifically, the Alternative View recognises Ramsey’s adoption of the logical pragmatist philosophy of C. S. Peirce from 1924 onwards with a dispositional theory of belief in which beliefs are treated not only as useful mental habits that can successfully guide future actions but also as able to provide true explanations of observed empirical facts. The textual evidence is examined, particularly Keynes’s biographical essay on Ramsey, which, it is argued, supports the contention that Keynes fully appreciated and was sympathetic to Ramsey’s pragmatism especially the importance of vague knowledge and the need for the development of human logic as the study of reasonable human behaviour.

Translating victims’ perceptional variations into policy recommendations in the context of riverine floods in a tropical region

The study of human behaviour and perceptions regarding flood management strategies can help enhance the adaptive capacity of flood victims. An in-depth questionnaire survey was executed over a statistically significant sample size of 2382 households to assess the perception of flood victims regarding 27 parameters of existing flood management strategies in a tropical flood-prone region like the Mayurkashi River Basin, India. The techniques such as ridit analysis, spatial autocorrelation, Kruskal-Wallis test, principal component analysis, and Lewin's equation of human behaviour have been used to reveal the complexity of the perceptional pattern. The results found that there are four existing flood management strategies (1) local community initiatives, (2) initiatives taken by the government and others, (3) joint (local community and government) structural, and (4) joint non-structural measures. Ridit analysis depicted that perceptional variations are relatively higher for the indicator under government and other initiatives while relatively lower for the indicators under the local community initiatives. The higher perceptional variations for the parameters of the government initiatives imply the existence of a top-down approach. However, parameters under community initiatives register a lower perceptional variability indicating the relative objectivity in their opinions. The study also found that characteristics of perceivers (socio-economic traits), situation (flood event), and target (nature of management strategies) have been instrumental in driving the perceptional variation. Finally, the study emphasizes the need for the blending of the bottom-top approach with the existing top-down approaches in village-level planning in a participatory manner.

Connecting livestock disease dynamics to human learning and biosecurity decisions.

The acceleration of animal disease spread worldwide due to increased animal, feed, and human movement has driven a growing body of epidemiological research as well as a deeper interest in human behavioral studies aimed at understanding their interconnectedness. Biosecurity measures can reduce the risk of infection, but human risk tolerance can hinder biosecurity investments and compliance. Humans may learn from hardship and become more risk averse, but sometimes they instead become more risk tolerant because they forget negative experiences happened in the past or because they come to believe they are immune. We represent the complexity of the hog production system with disease threats, human decision making, and human risk attitude using an agent-based model. Our objective is to explore the role of risk tolerant behaviors and the consequences of delayed biosecurity investments. We set up experiment with Monte Carlo simulations of scenarios designed with different risk tolerance amongst the swine producers and we derive distributions and trends of biosecurity and porcine epidemic diarrhea virus (PEDv) incidence emerging in the system. The output data allowed us to examine interactions between modes of risk tolerance and timings of biosecurity response discussing consequences for disease protection in the production system. The results show that hasty and delayed biosecurity responses or slow shifts toward a biosecure culture do not guarantee control of contamination when the disease has already spread in the system. In an effort to support effective disease prevention, our model results can inform policy making to move toward more resilient and healthy production systems. The modeled dynamics of risk attitude have also the potential to improve communication strategies for nudging and establishing risk averse behaviors thereby equipping the production system in case of foreign disease incursions.

Words Similarities on Personalities: A Language-Based Generalization Approach for Personality Factors Recognition

The evaluation of personality traits allows the study of human behavior in different environments, but it is not a trivial task. In this sense, the Five-Factor Model (FFM) allows, in a global way, the assessment of personality traits of individuals using textual data. However, there is a scarcity of lexical resources for languages other than English, which generated the main research question of this work: "Can models trained to predict FFM personality traits using English textual data show satisfactory results when applied to textual data in other languages?". Therefore, this work aims to answer: (i) Whether Word Embeddings techniques could be used to solve low resources languages problems in FFM personality traits prediction; and (ii) Whether is feasible to train a traditional Machine Learning algorithm with English language textual data and evaluate its performance with Brazilian Portuguese language textual data for FFM personality traits prediction. Thus, the work aims to present an approach in which the models can be used to learn the highest level of abstraction. As results, we observed that the difference in performance between the models trained for personality recognition in English is minimal when used to predict FFM personality traits in Brazilian Portuguese texts. In this task, the Stochastic Gradient Descent model presents the best average results among the FFM personality traits of the models analyzed.

Study and Prediction of Human Behavior Based on Face-Recognition

Study and Prediction of Human Behavior Based on Face-Recognition

From rodents to humans: Rodent behavioral paradigms for social behavioral disorders.

Social cognition guides social behavior. Subjects with proper social cognition should be able to: (1) have reasonable social motivation, (2) recognize other people and infer their intentions, and (3) weigh social hierarchies and other values. The choice of appropriate behavioral paradigms enables the use of rodents to study social behavior disorders in humans, thus enabling research to go deeper into neural mechanisms. This paper reviews commonly used rodent behavioral paradigms in studies of social behavior disorders. We focused specifically on sorting out ways to transfer the study of human social behavior to rodents through behavioral paradigms.

Psychology Professionals in Educational Contexts: An Unavoidable Necessity

Mental health problems and psychological adjustment difficulties among young people are emerging as a personal, family, educational, social, and health challenge. It is necessary to address this emerging social defiance by studying and implementing strategies to promote emotional well-being and prevent psychological problems in educational contexts. The aim of this work is to highlight, based on the epidemiological data derived from the PSICE (Evidence-based Psychology in Educational Contexts) project, the importance of addressing mental health in school settings. First, an introduction to the study of human behavior and psychological adjustment problems is given. Secondly, the importance of educational centers as the natural place to promote the psychological well-being and child and adolescent mental health is discussed. Thirdly, the prevalence results derived from the PSICE study are mentioned. To conclude, a summary section highlights the imperative need to promote, protect, and care for mental health during childhood and adolescence in educational settings. The promotion of psychological well-being and the prevention of mental health problems in educational contexts are the best investments on the part of societies, governments, and institutions.