Human Behavioral Responses Research Articles

Digital twin simulation modelling shows that mass testing and local lockdowns effectively controlled COVID-19 in Denmark

BackgroundFollowing the COVID-19 pandemic, it is important to evaluate different mitigation strategies for future preparedness. Mass testing and local lockdowns were employed during the Alpha wave in Denmark, which led to ten times more tests than the typical European member state and incidence-based restrictions at the parish level. This study aims to quantify the effects of these interventions in terms of hospital admissions and societal freedom.MethodsThis study assesses the effectiveness of these strategies via counterfactual scenarios using a detailed, individual-based simulation model that replicates the entire Danish population. The model considers multiple factors, including evolving societal restrictions, vaccination roll-out, seasonal influences, and varying intensities of PCR and antigen testing across different age groups and degree of completed vaccination. It also integrates adaptive human behavior in response to changes in incidences at the municipality and parish levels.ResultsThe simulations show, that without mass testing in Denmark, there would have been a 150% increase in hospital admissions, and additional local lockdowns equivalent to 21 days of strict national lockdown. Without the policy of local lockdowns, hospitalizations would have increased by 50%.ConclusionsIn conclusion, the combination of mass testing and local lockdowns likely prevented a large increase in hospitalizations while increasing overall societal freedom during the Alpha wave in Denmark. In future epidemics, mass testing and local lockdowns can likely prevent overwhelming healthcare systems in phases of high transmission and hospitalization risks.

Exploring the Impact of Visual and Aural Elements in Urban Parks on Human Behavior and Emotional Responses

As cities progress into high-quality developments, the demand for urban parks that enhance residents’ well-being and sustainability is increasing. Traditional visual-centric design methods no longer suffice. Given that vision and hearing are the primary sensory pathways through which people perceive their environment, exploring their relationship with landscape experiences offers a novel perspective for optimizing the audiovisual perception quality of urban parks. This study explores the relationship between visual and auditory elements and landscape experiences to optimize urban parks’ sensory quality. Using visual perception, soundscape perception, sound source perception, and behavioral vitality, this study evaluates the audiovisual perception quality of a representative wetland park in Chengdu’s ring ecological zone. By quantifying relationships between audiovisual characteristics, behavioral vitality, and emotional feedback, several emotional assessment models were constructed. The results show that lawns, pavements, and sound pressure levels significantly impact vitality. A sound pressure level of 77 dB has been identified as a critical threshold in emotional perception models. Consequently, distinct emotional prediction models can be employed to enhance landscape design across various sound pressure level zones. This research provides scientific evidence and flexible strategies for designing urban open spaces that improve landscape experiences based on multisensory perception.

Infectious disease responses to human climate change adaptations.

Many recent studies have examined the impact of predicted changes in temperature and precipitation patterns on infectious diseases under different greenhouse gas emissions scenarios. But these emissions scenarios symbolize more than altered temperature and precipitation regimes; they also represent differing levels of change in energy, transportation, and food production at a global scale to reduce the effects of climate change. The ways humans respond to climate change, either through adaptation or mitigation, have underappreciated, yet hugely impactful effects on infectious disease transmission, often in complex and sometimes nonintuitive ways. Thus, in addition to investigating the direct effects of climate changes on infectious diseases, it is critical to consider how human preventative measures and adaptations to climate change will alter the environments and hosts that support pathogens. Here, we consider the ways that human responses to climate change will likely impact disease risk in both positive and negative ways. We evaluate the evidence for these impacts based on the available data, and identify research directions needed to address climate change while minimizing externalities associated with infectious disease, especially for vulnerable communities. We identify several different human adaptations to climate change that are likely to affect infectious disease risk independently of the effects of climate change itself. We categorize these changes into adaptation strategies to secure access to water, food, and shelter, and mitigation strategies to decrease greenhouse gas emissions. We recognize that adaptation strategies are more likely to have infectious disease consequences for under-resourced communities, and call attention to the need for socio-ecological studies to connect human behavioral responses to climate change and their impacts on infectious disease. Understanding these effects is crucial as climate change intensifies and the global community builds momentum to slow these changes and reduce their impacts on human health, economic productivity, and political stability.

Human behavioral response to earthquake early warnings (EEW): Are alerts received on mobile phones inhibiting protective actions?

Earthquake Early Warning (EEW) systems alert residents in locations likely to be shaken by large earthquakes. Despite the short warning period from the reception of an EEW until the arrival of shaking, residents are expected to protect themselves during the available time. Contrary to such expectations, previous research has revealed that most people did not take protective actions after receiving an EEW. Additionally, it was revealed that most people received EEW messages via mobile phones. This study empirically examined the relationship between receiving an EEW on mobile phones and taking protective actions. In the first case, a survey (N = 3000) was conducted to investigate residents’ reactions immediately after receiving an EEW issued for the Tokyo metropolitan area on October 7, 2021, for an earthquake of magnitude 6.0. The results suggested that the most common reaction upon receiving an EEW was to consult mobile phones to better understand the message. Thus, mobile phones receive more attention than the impending danger, resulting in the loss of precious seconds that could be used to take protective actions. In the second case, a survey (N = 1000) was conducted on another EEW issued for an earthquake of magnitude 6.3, which occurred in Ishikawa Prefecture on May 5, 2023. The results were consistent with those of the first survey, suggesting that the findings described above are robust. Finally, this study discusses ways to mitigate the dangers associated with earthquakes based on the findings of these studies.

Emotion Based Crime Track

Human emotions, which are primarily conveyed through nonverbal communication such as facial expressions and body language, can be challenging to decipher due to their nuanced nature. For instance, an angry individual might avoid making eye contact, clench their fists, or display a furrowed brow, while a person experiencing joy may exhibit a wide smile, relaxed posture, and lively gestures. Similarly, the subtleties in the facial expressions and emotions of potential criminals can be identified using Artificial Intelligence (AI) techniques. These AI methodologies involve several steps: initially capturing the face, then detecting and testing the face for various expressions, followed by facial recognition, and finally analyzing facial ratios to identify criminals or specific individuals based on behavioral cues and patterns. Utilizing the latest advancements in technology, the Python programming language, in conjunction with Convolutional Neural Networks (CNN) algorithms, is employed to analyze a person’s emotions with remarkable accuracy and efficiency. This project introduces a cutting-edge technique known as Facial Emotion Recognition using Convolutional Neural Networks (FERC). FERC operates in two primary phases within the CNN framework: the first phase aims to remove the background from the image, while the second focuses on the intricate details of facial features, allowing the system to extract and analyze these features with precision. The culmination of this process is the detection of the subject's emotion, which is subsequently presented in a comprehensive graphical report. This report showcases the detected emotions over time, providing valuable insights into the individual’s emotional state, enabling researchers, psychologists, and even law enforcement agencies to better understand human behavior and emotional responses in various contexts.

Stereotypical ideas in the perception of spatial marginality of urban outskirts

According to the territorial-regional development policy of Russia, a rigid structuring of the country's space is applied through the prism of a hierarchical management system. As a result, the structure of the space of the entire country was fixed through a system of boundaries, the markers of which highlight management objects at the national level, subject of the Russian Federation, territory, region, district. This article will present the main stereotypes that influence the creation of the perception of new territories in the nature of marginality. As is known, the formation of marginal communities can be traced throughout the history of civilizations. It is important to separate geographical marginality from spatial marginality. In geography, there is the concept of “marginal territories”, which can be considered those located on the remote periphery of the region or in isolated places. Such a phenomenon as spatial marginality is characterized precisely by the prevailing stereotypes in society about a specific area. Urban spatial perception critically influences human behavior and emotional responses, emphasizing the need to align urban spaces with human needs to improve the quality of urban life. However, the classification of urban architecture based on functionality is subject to biases stemming from discrepancies between objective representation and subjective perception. These biases can lead to city planning and designs that fail to adequately meet the needs and preferences of city residents, negatively impacting their quality of life and the overall functionality of the city. In this study, we apply machine learning to uncover these biases in urban spatial perception research using a three-step methodology: objective mapping, subjective perception analysis, and perceptual bias assessment. Our results show that machine learning can reveal hidden patterns in this area of research with significant implications for urban planning and design. Of particular note, the study found significant discrepancies in the distribution centroids between commercial buildings and residential or public buildings. This result sheds light on the spatial organization characteristics of urban architectural functions, serving as a valuable guide for urban planning and development. Moreover, it reveals the advantages and disadvantages of different data sources and methods for interpreting urban spatial perception, paving the way to a more complete understanding of the subject. These results highlight the importance of integrating both objective mapping and subjective perspectives when classifying the functionality of urban architecture.

Artificial Intelligence-based Hyper Activity Analysis

Sentiment analysis has emerged as a valuable tool for analyzing human behavior and measuring frustration levels. This abstract provides an overview of the sentiment analysis of human behavior in response to measuring frustration levels. By examining the emotional tone expressed in textual data, sentiment analysis techniques offer insights into individuals' frustration levels, contributing to a better understanding of their psychological wellbeing. This study focuses on the application of sentiment analysis in measuring frustration levels and understanding human behavior. It explores the limitations and challenges associated with accurately assessing frustration based on Textual data, Psychological Questionnaires, Image Processing, Tone or speech, and Augmented Reality. The study acknowledges the importance of context and the need to account for linguistic nuances, sarcasm, and individual differences in language use. It also emphasizes the significance of considering additional modalities, such as facial expressions and virtual reality, to enhance the accuracy and reliability of measuring frustration levels.

A survey of perceived risk and protective behaviours towards COVID-19 in 2020 in Iraqi Kurdistan.

Human perceptions and behavioural responses to the risk of epidemics have always been crucial factors in studying and containing disease spread. This study aims to assess and understand the risk perception and the behavioural response of a sample of the population in the Kurdistan Region toward COVID-19. A self-administered online survey (designed on Google Forms) was designed to get a rapid appraisal of the risk perception and behavioural response of people living in the Iraqi Kurdistan Region about COVID-19 from 17-25 November 2020. A total of 390 individuals responded to the survey. Many respondents (65.6%) recognized COVID-19 as a high or very high threat level. The most frequently applied protective behaviours included avoiding spitting on the ground (76.2%), avoiding contacting ill persons (75.9%) and wearing face masks (75.6%). The main source of information about COVID-19 was Internet news (46.2%), followed by social media (44.9%). Significantly higher perceptions of the threat of COVID-19 were found among females (p = 0.004) and those having family members with chronic diseases (p < 0.001). Understanding the general public's risk perception toward the COVID-19 infection is essential for determining effective protective measures and can be used to guide proper preventive behaviour.

Characterizing Human–Automated Vehicle Interactions: An Investigation into Car-Following Behavior

Automated vehicles are expected to influence human drivers’ behavior. Accordingly, capturing such changes is critical for planning and operation purposes. With regard to car-following behavior, a key question is whether existing car-following models can replicate these changes in human behavior. Using a data set that was collected from the car-following behavior of human drivers when following automated vehicles, this paper offers a robust methodology based on the concept of dynamic time warping to investigate the critical parameters that can be used to capture changes in human behavior. The results indicate that spacing can best substantiate such changes. Moreover, calibration and validation of the intelligent driver model (IDM) suggest its inability to capture changes in human behavior in response to automated vehicles. Thus, an extension of the IDM that explicitly models stochasticity in the behavior of individual drivers is applied, and the results show such a model can identify a reduction in uncertainty when following an automated vehicle. This finding also has implications for a stochastic extension to other models when analyzing and simulating a mixed-autonomy traffic flow environment.

The Human Superorganism: Using Microbes for Freedom vs. Fear

Balanced fear supports human rational decision-making and useful behavioral responses. In contrast, overwhelming, persistent, and unbalanced fear can paralyze the individual and result in heightened anxiety, lack of cognitive flexibility, fear-based public compliance and serious mental health issues. Psychobiotics research has established that a healthy microbiome is required for balanced fear and mental health protection via control of fear extinction. The recent COVID-19 pandemic featured daily, persistent, fear-of-a-single-contagion conditioning on a global scale paired with various behavioral mandates (e.g., lockdowns of the healthy, required wearing of face masks in many locations including schools, isolation from environmental microbes and each other through the closure of beaches and parks, and restrictions on social gatherings including access to family members in hospitals and senior-assisted facilities). Such mandates degraded the human microbiome and isolated us from each other and useful environmental microbes. It also ignored the historic role of secondary bacterial pathogens in pandemic deaths. This narrative review examines how the institutional promotion of fear-of-a-single-contagion, lack of balanced risk communication, and appalling disregard of our fundamental nature (as majority-microbial human superorganisms) resulted in problems rather than solutions. This review illustrates that government-public health-media promotion of pervasive fear and microbiome-degrading behaviors: (1) increased public compliance, (2) reduced cognitive flexibility, and (3) increased risk of mental health conditions. However, a portion of the general public chose a healthier path through their increased consumption of microbiome- and immune-supportive supplements and fermented foods during and after the COVID-19 pandemic. For a healthier future, public health must follow the lead of this population to ensure that human freedom, rather than paralyzing fear, dominates our future.

The effects of the natural visual-aural attributes of urban green spaces on human behavior and emotional response.

Nature-based solutions (NBS) have been used to address a wide range of urban environmental challenges, an important aspect of which is to improve human health and well-being. However, most relevant studies focus either on what positive influences nature may have or on identifying what natural factors can have these benefits. Few have investigated the sensory composition of nature and the effects of nature in different sensory aspects on human health. Setting out from the multi-sensory perspective, this study aims to explore human behavior and emotional response from visual and aural contact with urban nature. Taking Jiangjia Art Garden in Chengdu as an example, natural attributes such as its visual (landscape) and aural (sound source) characteristics as well as people's activities (behavioral responses) were measured by on-site mapping analysis. This was done while a questionnaire-based survey was conducted to investigate people's emotional responses regarding their overall satisfaction, pleasantness, calm, and agreeableness. The results indicated that nature-dominated visual landscapes such as grassland, waterscapes, and woodlands, as well as natural sounds such as bird sounds, chirp sounds, and wind sounds were found to be positively correlated to the vitality of activities and people's emotional status. Regarding behavioral responses, it was shown that grasslands and woodlands are more likely to be attractive places for recreation, and the vitality measured became extremely high when these two were paired with lakes. As for the emotional responses, people's perceived overall satisfaction, calm, and agreeableness were equally reflected in their behavioral patterns, suggesting a strong relationship with natural factors. The research findings were visually presented in behavior and emotional maps to provide direct cues of informing the future design of high-quality urban green spaces and promoting the application of aural-visual experience in the design of urban nature areas.

Late Holocene tule elk (Cervus canadensis nannodes) resource depression and distant patch use in central California: Faunal and isotopic evidence from King Brown and the Emeryville Shellmound

Previous research has documented declines in the abundance of high-return resources including tule elk (Cervus canadensis nannodes) over the past three millennia in central California, suggesting the occurrence of resource depression. We test the hypothesis that hunting depressed tule elk in this setting by articulating stable isotope analyses from 88 directly dated tule elk specimens with data on the age structure and skeletal part representation from the King Brown and Emeryville Shellmound sites. Late Holocene trends in stable isotopes and modeled climatic variation are inconsistent with climate-based population declines. However, at King Brown, located within the Central Valley and vast tule elk freshwater marsh and grassland habitat, increasing isotopic diversity, a decline in mean age, and increasing abundance of high-utility skeletal elements suggest local depression stimulated the increasing use of distant elk patches. Although faunal trends are consistent with the depression of elk at Emeryville, the site is located on the shore of the San Francisco Bay where limited elk habitat existed, and no evidence of distant elk patch use is indicated. This analysis underscores how human behavioral responses to resource depression can vary in relation to the local ecology as they affect patch use economics for specific prey and demonstrates how such responses can be deciphered through stable isotope and faunal data.

COVID-19: Epidemiological Models

The world has been confronted in recent decades with several infectious disease outbreaks caused by novel pathogens, with the COVID-19 pandemic being the most severe of these. In this article, I review some of the main elements of epidemiological models used to forecast the trajectory of a new epidemic and to guide public health policy responses to a new infectious disease. I argue that economists have a lot to contribute to the discussion of public health policies, particularly in regard to assessing the costs and benefits of alternative policies and in improving the modeling of changes in human behavior in response to new infectious diseases. This survey is intended to serve economists interested in starting research in these areas.

Motivational decline and proactive response under thermal environmental stress are related to emotion- and problem-focused coping, respectively: Questionnaire construction and fMRI study.

Despite the diversity of human behavioral and psychological responses to environmental thermal stress, the major dimensions of these responses have not been formulated. Accordingly, the relevance of these responses to a framework of coping with stress (i.e., emotion- and problem-focused) and the neural correlates are unexplored. In this study, we first developed a multidimensional inventory for such responses using social surveys and a factor analysis, and then examined the neural correlates of each dimension using a functional magnetic resonance imaging; we manipulated the ambient temperature between uncomfortably hot and cold, and the correlations between the inventory factor scores and discomfort-related neural responses were examined. We identified three factors to construct the inventory: motivational decline, proactive response, and an active behavior, which appeared to reflect inefficient emotion-focused coping, efficient problem-focused coping, and positive appreciation of extreme environmental temperatures, respectively, under environmental thermal stress. Motivational decline score was positively associated with common neural response to thermal stress in the frontal and temporoparietal regions, implicated in emotion regulation, while proactive response score negatively with the neural responses related to subjective discomfort in the medial and lateral parietal cortices, implicated in problem-solving. We thus demonstrated that two of three major dimensions of individual variation in response to and coping with environmental thermal stress conform to an influential two-dimensional framework of stress coping. The current three-dimensional model may expand the frontiers of meteorological human science in both basic and application domains.

Exploring epidemic voluntary vaccinating behavior based on information-driven decisions and benefit-cost analysis

A complex dynamic interplay exists between epidemic transmission and vaccination, which is significantly influenced by human behavioral responses. We construct a research framework combining both the function modeling of the cumulative global COVID-19 information and limited individuals’ information processing capacity employing the Gompertz model for growing processes. Meanwhile, we built a function representing the decision to get vaccinated following benefit-cost analysis considered the choices made by people in each scenario have an influence from altruism, free-riding and immunity escaping capacity. Through the mean-field calculation analysis and using a fourth-order Runge-Kutta method with constant step size, we obtain plots from numerical simulations.We found that only when the total number of infectious individuals proves sufficient to reach and exceed a certain level will the individuals face a better trade-off in determining whether to get vaccinated against the diseases based on that information. Besides, authoritative media have a higher decisive influence and efforts should be focused on extending the duration of vaccine protection, which is beneficial to inhibit the outbreaks of epidemics. Our work elucidates that reducing the negative payoff brought about by the free-riding behavior for individuals or improving the positive payoff from the altruistic motivation helps to control the disease in cultures that value social benefits, vaccination willingness is generally stronger. We also note that at a high risk of infection, the decision of vaccination is highly correlated with global epidemic information concerning COVID-19 infection, while at times of lower risk, it depends on the game theoretic vaccine strategy. The findings demonstrate that improving health literacy, ensuring open and transparent information on vaccine safety and efficacy as a public health priority can be an effective strategy for mitigating inequalities in health education, as well as alleviating the phenomenon that immunity escaping abilities is more likely to panic by populations with high levels of education. In addition, prosocial nudges are great ways to bridge these immunity gaps that can contribute to implementing government public health control measures, creating a positive feedback loop.

Human thirst behavior requires transformation of sensory inputs by intrinsic brain networks

BackgroundTo survive and thrive, many animals, including humans, have evolved goal-directed behaviors that can respond to specific physiological needs. An example is thirst, where the physiological need to maintain water balance drives the behavioral basic instinct to drink. Determining the neural basis of such behaviors, including thirst response, can provide insights into the way brain-wide systems transform sensory inputs into behavioral outputs. However, the neural basis underlying this spontaneous behavior remains unclear. Here, we provide a model of the neural basis of human thirst behavior.ResultsWe used fMRI, coupled with functional connectivity analysis and serial-multiple mediation analysis, we found that the physiological need for water is first detected by the median preoptic nucleus (MnPO), which then regulates the intention of drinking via serial large-scale spontaneous thought-related intrinsic network interactions that include the default mode network, salience network, and frontal-parietal control network.ConclusionsOur study demonstrates that the transformation in humans of sensory inputs for a single physiological need, such as to maintain water balance, requires large-scale intrinsic brain networks to transform this input into a spontaneous human behavioral response.

Effects of the Federal Aviation Administration's Compliance Program on aircraft incidents and accidents

• Restorative justice-based policies in aviation may be unsafe. • The FAA’s Compliance Program may reduce aviation safety. • Population-level data was evaluated using GLARMA modeling. • An increase in aircraft incidents and accidents resulted from the FAA’s Compliance Program. • A decrease in punishment may affect human behavioral responses. A novel enforcement policy based on restorative justice principles was implemented by the United States Federal Aviation Administration (FAA) in 2015. In this study, interrupted time series regression analyses using generalized linear autoregressive moving average (GLARMA) models were used to analyze FAA investigated incident and accident counts, runway incursion caused by pilot deviation, and National Transport Safety Board (NTSB) accident count data from June 2010 to September 2020. An association was observed between the change in FAA’s enforcement policy and the increase in aircraft incidents, accidents, and runway incursions post-intervention (2015–2020). These adverse safety outcomes appear to be directly related to this policy change. Results highlight the risk of moving away from punishment as a means of reinforcing safe behavior and the need for careful theory-based implementation of alternative approaches.

Human behavioral response to fluctuating automation reliability

Human perception of automation reliability and automation acceptance behaviours are key to effective human-automation teaming. This study examined factors that impact perceptions of automation reliability over time and the acceptance of automated advice. Participants completed a maritime vessel classification task in which they classified vessels (contacts) with the assistance of automation. In Experiment 1 automation reliability successively switched from high to low (or vice versa). In Experiment 2 automation reliability decreased by varying magnitudes before returning to high. Participants did not initially calibrate to true reliability and experiencing low automation reliability reduced future reliability estimates when experiencing subsequent high reliability. Automation acceptance was predicted by positive differences between participant perception of automation reliability and confidence in their own manual classification reliability. Experiencing low automation reliability caused perceptions of reliability and automation acceptance rates to diverge. These findings have important implications for training and adaptive human-automation teaming in complex work environments.

An Empirical Investigation of Virtual Networking Sites Discontinuance Intention: Stimuli Organism Response-Based Implication of User Negative Disconfirmation.

The study’s prime objective is to investigate the user discontinuance intention in the shed of the negative disconfirmation of user expectation. The study has derived the theoretical structure from the expectancy disconfirmation theory (EDT) enacted through the stimuli organism response (SOR) framework to study the actual cause and effect relationship of human behavioral response. To investigate the user discontinuance intention behavioral response, a total of 434 correct and complete answers were shortlisted for analysis. To examine the data set, the study has used the modern partial least square method technique or simply SmartPLS service package to run the structural equation modeling (SEM). Moreover, the study has implied the 80/20 rule run the mediating analysis of the SOR framework. The statistical results show that all three stimuli make significant positive disconfirmation of the user beliefs in terms of dissatisfaction and the anxiety that ultimately leads to the discontinuance intention in virtual network users. Further, these results are validated through the six mediating relationships, which partially mediate the relationship between the stimuli and response. Besides all these findings, this study has made some practical and realistic theoretical and practical implications for both researchers and service-providing managers.

Evaluating human behaviour in response to AI recommendations for judgemental forecasting

Various advanced systems deploy artificial intelligence (AI) and machine learning (ML) to improve demand forecasting. Supply chain planners need to become familiar with these systems and trust them, considering real-world complexities and challenges the systems are exposed to. However, planners have the opportunity to intervene based on their experience or information that the systems may not capture. In this context, we study planners’ adjustments to AI-generated demand forecasts. We collect a large amount of data from a leading AI provider and a large European retailer. Our dataset contains 30 million forecasts at the SKU-store-day level for 2019, plus variables related to products, weather, and holidays. In our two-phase analysis, we aim to understand the adjustments made by planners and the quality of these adjustments. Within each phase, we first identify the drivers of adjustments and their quality using random forest, a well-known ML algorithm. Next, we investigate the collective effects of the different drivers on the occurrence and the quality of the adjustments using a decision tree approach. We find that product characteristics such as price, freshness, and discounts are important factors when making adjustments. Large positive adjustments occur more frequently but are often inaccurate, while large negative adjustments are generally more accurate but fewer in number. Thus, planners do not contribute to accuracy on average. Our findings provide insights for the better use of human knowledge in judgmental forecasting.