Human Behavior Research Articles

Nudging visitors into pro-environmental behaviour: assessing the use of a tidal information pillar for effective dynamic zoning

In the past few years, more human-wildlife conflicts have occurred as a result of increased human visitations, particularly in areas where space is scarce and functions need to co-exist. Nature organisations are increasingly challenged to improve nature protection on the one hand, while offering visitors high quality nature experiences on the other hand. To encourage both go hand in hand, nudging has recently received more attention. Nudging is a tool to subtly change human behaviour without imposing strict rules. It is, however, hardly applied in nature areas. Currently, experiments are taking place in the Dutch part of the Wadden Sea with alternating signage (nudging message based on real-time data of the area) aided by a tidal information pillar to encourage dynamic zoning. At low tide the signage indicates: freely accessible, at high tide the advice: ‘do not enter’. The purpose is to address visitors’ sense of responsibility, nudge pro-environmental behaviour and reduce human-wildlife conflicts by limiting access. This article assesses the effectiveness of the alternating nudging signage, and especially the effectiveness of the signage ‘high tide – do not enter’. The results show that the signage on the pillar is most effective when visitors stop at the pillar and read the advice. Among these visitors, about half turn around. Among the visitors who do not encounter the signage and the pillarn, the majority goes on to access the area. With this study, we aim to contribute to the knowledge on nudging as a tool to promote pro-environmental behaviour in nature areas.

Dissecting of the two-stages object detection models architecture and performance

Artificial intelligence (AI) is the discipline focused on enabling computers to operate autonomously without explicit programming. Within AI, computer vision is an emerging field tasked with endowing machines with the ability to interpret visual data from images and videos. Over recent decades, computer vision has found applications in diverse fields such as autonomous vehicles, information retrieval, surveillance, and understanding human behavior. Object detection, a key aspect of computer vision, employs deep neural networks to continually advance detection accuracy and speed. Its goal is to precisely identify objects within images or videos and assign them to specific classes. Object detection models typically consist of three components: a backbone network for feature extraction, a neck model for feature aggregation, and a head for prediction. The focus of this study lies on two stage detectors. This study aims to provide a comprehensive review of two stage detectors in object detection, followed by benchmarking to offer insights for researchers and scientists. By analyzing and understanding the efficacy of these models, this research seeks to guide future developments in the field of object detection within computer vision.

Artificial Intelligence: A Quick Overview of Speech Recognition System

Abstract: The study of intelligent behaviour is known as artificial intelligence. Wagner said, "We build intelligent machines in science." By this, he implied that the primary goal is to teach these sophisticated devices, such as computers, to examine and comprehend human conduct. numerous believed that human intelligence might be understood through the construction of several programmes, while other researchers believed that numerous basics should be required in order to accomplish the intended objective. We can carry out or replicate any type of task we conduct using a computer. Therefore, computers are the ideal devices for developing artificial intelligence. In the hopes that they will outperform computers in several other areas, some researchers created additional computing devices. Billions of dollars were invested in creating new devices that surpassed the speed of the computer used to simulate software. However, the computers and the code both need to be extremely quick. As you learn about artificial intelligence, you may wonder, what really is intelligence? How is intelligence measured? Turn become something quite significant. In this case, the programme that evaluates its surroundings acts as the intelligent agent. Modern digital computers and human minds are comparable to one other in ways such as symbolic information processing systems. Both use symbolic data as input, change it in accordance with predetermined guidelines, and then use the results to solve issues. In order to mechanically replicate intelligent human activity, artificial intelligence researchers have programmed some algorithms to recognise intelligent human behaviour. Human conduct in language processing, chess, and other games, as well as medical diagnostics, may be used to monitor this activity. Voice recognition is a key component of AI. A certain voice may be recognised using voice recognition technology. The cornerstone for speaker identification is voice signals. Voice targeting is applicable to a wide range of applications, including voice mail, database access, phone banking, and phone purchasing. The ability to enter one's voice for verification is among the most potent uses of voice recognition for security. The fundamental means of interpersonal communication is speech. The technique of translating voice sounds into appropriate text is known as speech recognition. Over the past few years, speech recognition technology has advanced significantly. Nonetheless, there are other significant study obstacles, such as variations in speaker and language, ambient sound, word size, etc. This study aims to give a comprehensive overview of speech acceptance by summarising the several approaches utilised in the standard speech system and describing the numerous processes involved

Establishing connectivity through microdissections of midbrain stimulation-related neural circuits.

Comprehensive understanding of the neural circuits involving the ventral tegmental area is essential for elucidating the anatomo-functional mechanisms governing human behaviour as well as the therapeutic and adverse effects of deep brain stimulation for neuropsychiatric diseases. While the ventral tegmental area has been successfully targeted with deep brain stimulation for different neuropsychiatric diseases, the axonal connectivity of the region has not been fully understood. Here using fiber micro-dissections in human cadaveric hemispheres, population-based high-definition fiber tractography, and previously reported deep brain stimulation hotspots, we find that the ventral tegmental area participates in an intricate network involving the serotonergic pontine nuclei, basal ganglia, limbic system, basal forebrain, and prefrontal cortex, which is implicated in the treatment of obsessive-compulsive disorder, major depressive disorder, Alzheimer's disease, cluster headaches, and aggressive behaviors.

Application of Hybrid Deep Reinforcement Learning for Managing Connected Cars at Pedestrian Crossings: Challenges and Research Directions

The autonomous vehicle is an innovative field for the application of machine learning algorithms. Controlling an agent designed to drive safely in traffic is very complex as human behavior is difficult to predict. An individual’s actions depend on a large number of factors that cannot be acquired directly by visualization. The size of the vehicle, its vulnerability, its perception of the environment and weather conditions, among others, are all parameters that profoundly modify the actions that the optimized model should take. The agent must therefore have a great capacity for adaptation and anticipation in order to drive while ensuring the safety of users, especially pedestrians, who remain the most vulnerable users on the road. Deep reinforcement learning (DRL), a sub-field that is supported by the community for its real-time learning capability and the long-term temporal aspect of its objectives looks promising for AV control. In a previous article, we were able to show the strong capabilities of a DRL model with a continuous action space to manage the speed of a vehicle when approaching a pedestrian crossing. One of the points that remains to be addressed is the notion of discrete decision-making intrinsically linked to speed control. In this paper, we will present the problems of AV control during a pedestrian crossing, starting with a modelization and a DRL model with hybrid action space adapted to the scalability of a vehicle-to-pedestrian (V2P) encounter. We will also present the difficulties raised by the scalability and the curriculum-based method.

Human control of AI systems: from supervision to teaming

AbstractThis article reviews two main approaches to human control of AI systems: supervisory human control and human–machine teaming. It explores how each approach defines and guides the operational interplay between human behaviour and system behaviour to ensure that AI systems are effective throughout their deployment. Specifically, the article looks at how the two approaches differ in their conceptual and practical adequacy regarding the control of AI systems based on foundation models––i.e., models trained on vast datasets, exhibiting general capabilities, and producing non-deterministic behaviour. The article focuses on examples from the defence and security domain to highlight practical challenges in terms of human control of automation in general, and AI in particular, and concludes by arguing that approaches to human control are better served by an understanding of control as the product of collaborative agency in a multi-agent system rather than of exclusive human supervision.

Modelling error chains in offshore wind energy systems: Examining the interplay of human performance and machine state

A dynamic model of the mutual interdependency between humans and machines in offshore wind farms (OWFs) is developed in this paper. The model emphasises the importance of having early indicators to dynamically moderate human behaviour and the need to account for both human and physical subsystems and their mutual interactions for optimal asset management practices. This work simulates three distinct scenarios for: (1) examining the interconnectedness of technical and human dynamics and their implications on error and failure; (2) assessing the impact of production loss on human and organisational behaviour; and (3) evaluating human error probability as an early warning sign of production loss. The findings suggest that adopting an appropriate maintenance culture and considering human error likelihood and production rate in decision-making leads to optimising production and mitigating the risks associated with human error. The research highlights the significance of comprehending the complex interactions between human and machine factors in the operation and maintenance (O&M) of offshore wind turbines (OWTs). The proposed dynamic model helps organisations identify the underlying causes of errors, allowing them to improve their maintenance strategies.

Surprise-minimization as a solution to the structural credit assignment problem.

The structural credit assignment problem arises when the causal structure between actions and subsequent outcomes is hidden from direct observation. To solve this problem and enable goal-directed behavior, an agent has to infer structure and form a representation thereof. In the scope of this study, we investigate a possible solution in the human brain. We recorded behavioral and electrophysiological data from human participants in a novel variant of the bandit task, where multiple actions lead to multiple outcomes. Crucially, the mapping between actions and outcomes was hidden and not instructed to the participants. Human choice behavior revealed clear hallmarks of credit assignment and learning. Moreover, a computational model which formalizes action selection as the competition between multiple representations of the hidden structure was fit to account for participants data. Starting in a state of uncertainty about the correct representation, the central mechanism of this model is the arbitration of action control towards the representation which minimizes surprise about outcomes. Crucially, single-trial latent-variable analysis reveals that the neural patterns clearly support central quantitative predictions of this surprise minimization model. The results suggest that neural activity is not only related to reinforcement learning under correct as well as incorrect task representations but also reflects central mechanisms of credit assignment and behavioral arbitration.

An Ultra-Conductive and Patternable 40nm-Thick Polymer Film for Reliable Emotion Recognition.

Understanding psychology is an important task in modern society which helps predict human behavior and provide feedback accordingly. Monitoring of weak psychological and emotional changes requires bioelectronic devices to be stretchable and compliant for unobtrusive and high-fidelity signal acquisition. Thin conductive polymer film has been regarded as an ideal interface; however, it is very challenging to be simultaneously balance mechanical robustness and opto-electrical property. Here, we report a 40nm-thick film based on photolithographic double-network conductive polymer mediated by graphene layer, which concurrently enables stretchability, conductivity and conformability. Photolithographic polymer and graphene endow the film photopatternability, enhance stress dissipation capability, as well as improving opto-electrical conductivity (4458 S cm-1 @ > 90% transparency) through molecular rearrangement by π-π, electrostatic interaction and hydrogen bonding. We further apply the film onto corrugated facial skin, monitor the subtle electromyogram, and perform machine learning algorithm to understand complex emotions, indicating the outstanding ability for stretchable and compliant bioelectronics. This article is protected by copyright. All rights reserved.

Higher surface folding of the human premotor cortex is associated with better long-term learning capability

The capacity to learn enabled the human species to adapt to various challenging environmental conditions and pass important achievements on to the next generation. A growing body of research suggests links between neocortical folding properties and numerous aspects of human behavior, but their impact on enhanced human learning capacity remains unexplored. Here we leverage three training cohorts to demonstrate that higher levels of premotor cortical folding reliably predict individual long-term learning gains in a challenging new motor task, above and beyond initial performance differences. Individual folding-related predisposition to motor learning was found to be independent of cortical thickness and intracortical microstructure, but dependent on larger cortical surface area in premotor regions. We further show that learning-relevant features of cortical folding occurred in close spatial proximity to practice-induced structural brain plasticity. Our results suggest a link between neocortical surface folding and human behavioral adaptability.

Beyond biophobia: positive appraisal of bats among German residents during the COVID-19 pandemic - with consequences for conservation intentions

AbstractBats are often considered to be objects of biophobia, i.e., the tendency to respond with a negative emotion, such as fear or disgust, especially during the COVID-19 pandemic. However, existing studies have rarely compared both positive and negative emotions towards bats, leading to a potential negativity bias. This is crucial given the importance of emotions to bat-related human behaviours, such as in bat conservation-related actions. Via two online surveys conducted among German residents, we aimed to (i) assess positive and negative emotions towards bats, (ii) examine emotional shifts during the pandemic and (iii) explore how emotions, along with socio-demographics, predict the intent to perform bat-conservation actions. The first survey was undertaken ten months after the official declaration of the COVID-19 pandemic (December 2020 - January 2021), when bats gained societal attention due to speculation about the origin of the SARS-CoV-2 virus, and the second one ran twelve months later (January 2022). Overall, respondents held higher positive emotions than negative ones towards bats in both surveys, with no significant emotional shift observed. Positive emotions positively correlated with intentions to perform bat-conservation actions, while negative emotions showed no such relationship. Although our findings might be context-specific to populations in Germany or Europe, given European-Union legislation protecting bats and their habitats, they highlight the nuanced and complicated emotions that can be associated with certain species. Understanding these emotions can guide targeted conservation strategies and public outreach. Our results caution against overly generalising discussions of biophobia in conservation.

UNCANNY ACTIVITY DETECTION USING CCTV MONITORING

"Uncanny Activity Detection Using CCTV Monitoring" introduces an innovative approach to support public safety through the integration of Machine Learning (ML) in real-time monitoring. In the contemporary world, Closed-Circuit Television (CCTV) surveillance stands as a fundamental and highly effective security measure for various premises, including hospitals, malls, and universities. It serves as a widely recognized tool for preventing and detecting unwanted activities. However, envisioning an public space equipped with several CCTV cameras across multiple buildings presents a logistical challenge. The manual monitoring of events across this expansive network is practically impossible. Furthermore, searching for a specific event in recorded video footage, even after it has occurred, proves to be a time-consuming endeavor. This project addresses the need for an efficient solution to manage and analyze extensive CCTV footage in complex environments, optimizing security practices and response times. Keywords: Anomaly Detection, Deep Learning, Human Behavior Recognition.

Analysis of Employee Motivations and Causes in the Workplace

Employee motivation refers to an employees drive to complete work-related tasks. Employee motivation is an essential factor that every employer needs to secure to encourage the completion of tasks in a dignified and effective manner. Intrinsic and extrinsic factors influence employee motivation. The paper shows that factors internal to the employee influence their reaction to their external workplace factors that can determine how motivated they are to deliver the mandates. The paper considers the two aspects of the attribution theory, situational and dispositional, which shows that a persons internal factors determine how they react to the external environment and respond to motivation. This paper also considers how deviant behavior explains the origins of human behavior to show how internal factors can affect how employees respond to motivation. Internal and external organizational factors can also affect the effectiveness of the companys motivation strategy. The theories help establish that any motivational intervention should consider an employees factors to ensure its success and positively impact the company and its operations.

Analytical Explanations of the Concept of the Fixed Term and the Random Error Term in Econometric Models

The error term or disturbance term plays an important role in the econometric model, and there are several reasons for including it in the econometric model, including issues related to measurement problems, the omission of variables from the econometric model, or for contingency reasons or related to abnormal human behavior from the general rule, or for inaccurate description of the models or inaccurate preparation of the data. The fixed term or intercept also has different meanings in the econometric model, such as the mathematical meaning, the econometric meaning, the fixed term without meaning, or the fixed term with a negative sign, and others.

Predicting game-induced emotions using EEG, data mining and machine learning

BackgroundEmotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal’s changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers’ performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions.ResultsThe RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases’ results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm’s capabilities in precisely determining the emotion label for each participant’s EEG features’ instance.ConclusionThe findings in this study fill the existing gap of game-induced emotion recognition field by providing an in-depth evaluation on the ruleset algorithm’s performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result.

A Critical Comparison of SEUT and Prospect Theory

This article explores two key theories in behavioral finance: Subjective Expected Utility Theory (SEUT) and Prospect Theory, outlining their development as responses to historic economic paradoxes, challenging traditional notions of rationality in economic decision-making. SEUT extends the concepts of expected value and rational choice, incorporating individual utility functions and subjective probabilities. Despite its comprehensive approach, SEUT struggles to explain certain behaviors under risk and uncertainty. Prospect Theory, conceived by Kahneman and Tversky, offers an alternative. It introduces decision weighting, focusing on how individuals differently perceive gains and losses relative to a reference point. This theory explains human biases like loss aversion and framing effects, which SEUT overlooks. Through practical examples and empirical studies, the article demonstrates scenarios where human behavior deviates from SEUT predictions, influenced by psychological factors. However, both theories provide insights into economic decision-making, they have their respective strengths and limitations. The article suggests a more integrated approach, recognizing the complexities of human decision-making in economic contexts.

Способы мифотворчества и их специфика (на примере художественных и публицистических текстов Умберто Эко)

The study aims to identify the specifics of myth-making techniques that are comprehended and tested by the 20th- and 21st-century European philosopher Umberto Eco in his numerous publicistic and literary texts. As a result of the study, classifications of the “traditional” ways of myth-making (the reference to traditional mythologized archetypes, the use of binary oppositions, etc.) and the “modern” ways (double coding, depersonalization, discreteness of history, etc.), determined by the cultural and historical context of postmodernism and post-structuralism were provided. Using these classifications, the significance and influence of myth and myth-making techniques on thinking, human behavior, and cultural and historical processes of past and modern eras were proven. The paper is novel in that by analyzing the complex of Umberto Eco’s texts, myth is considered as the most important construct both of human culture and human thinking. The mechanisms of constructing certain types of modern myths, as well as the ways of their influence on the mass consciousness, on historical and socio-cultural processes are described.

Portable Facial Expression System Based on EMG Sensors and Machine Learning Models

One of the biggest challenges of computers is collecting data from human behavior, such as interpreting human emotions. Traditionally, this process is carried out by computer vision or multichannel electroencephalograms. However, they comprise heavy computational resources, far from final users or where the dataset was made. On the other side, sensors can capture muscle reactions and respond on the spot, preserving information locally without using robust computers. Therefore, the research subject is the recognition of the six primary human emotions using electromyography sensors in a portable device. They are placed on specific facial muscles to detect happiness, anger, surprise, fear, sadness, and disgust. The experimental results showed that when working with the CortexM0 microcontroller, enough computational capabilities were achieved to store a deep learning model with a classification store of 92%. Furthermore, we demonstrate the necessity of collecting data from natural environments and how they need to be processed by a machine learning pipeline.

Balancing Human Likeness in Social Robots: Impact on Children’s Lexical Alignment and Self-disclosure for Trust Assessment

While there is evidence that human-like characteristics in robots could benefit child-robot interaction in many ways, open questions remain about the appropriate degree of human likeness that should be implemented in robots to avoid adverse effects on acceptance and trust. This study investigates how human likeness, appearance and behavior, influence children’s social and competency trust in a robot. We first designed two versions of the Furhat robot with visual and auditory human-like and machine-like cues validated in two online studies. Secondly, we created verbal behaviors where human likeness was manipulated as responsiveness regarding the robot’s lexical matching. Then, 52 children (7-10 years old) played a storytelling game in a between-subjects experimental design. Results show that the conditions did not affect subjective trust measures. However, objective measures showed that human likeness affects trust differently. While low human-like appearance enhanced social trust, high human-like behavior improved children’s acceptance of the robot’s task-related suggestions. This work provides empirical evidence on manipulating facial features and behavior to control human likeness in a robot with a highly human-like morphology. We discuss the implications and importance of balancing human likeness in robot design and its impacts on task performance, as it directly impacts trust-building with children.

Events in context—The HED framework for the study of brain, experience and behavior

The brain is a complex dynamic system whose current state is inextricably coupled to awareness of past, current, and anticipated future threats and opportunities that continually affect awareness and behavioral goals and decisions. Brain activity is driven on multiple time scales by an ever-evolving flow of sensory, proprioceptive, and idiothetic experience. Neuroimaging experiments seek to isolate and focus on some aspect of these complex dynamics to better understand how human experience, cognition, behavior, and health are supported by brain activity. Here we consider an event-related data modeling approach that seeks to parse experience and behavior into a set of time-delimited events. We distinguish between event processes themselves, that unfold through time, and event markers that record the experiment timeline latencies of event onset, offset, and any other event phase transitions. Precise descriptions of experiment events (sensory, motor, or other) allow participant experience and behavior to be interpreted in the context either of the event itself or of all or any experiment events. We discuss how events in neuroimaging experiments have been, are currently, and should best be identified and represented with emphasis on the importance of modeling both events and event context for meaningful interpretation of relationships between brain dynamics, experience, and behavior. We show how text annotation of time series neuroimaging data using the system of Hierarchical Event Descriptors (HED; https://www.hedtags.org) can more adequately model the roles of both events and their ever-evolving context than current data annotation practice and can thereby facilitate data analysis, meta-analysis, and mega-analysis. Finally, we discuss ways in which the HED system must continue to expand to serve the evolving needs of neuroimaging research.