Human Behavior Research Articles

Exploring the effectiveness of reward-based learning strategies for second-language speech sounds.

Adults struggle to learn non-native speech categories in many experimental settings (Goto, Neuropsychologia, 9(3), 317-323 1971), but learn efficiently in a video game paradigm where non-native speech sounds have functional significance (Lim & Holt, Cognitive Science, 35(7), 1390-1405 2011). Behavioral and neural evidence from this and other paradigms point toward the involvement of reinforcement learning mechanisms in speech category learning (Harmon, Idemaru, & Kapatsinski, Cognition, 189, 76-88 2019; Lim, Fiez, & Holt, Proceedings of the National Academy of Sciences, 116, 201811992 2019). We formalize this hypothesis computationally and implement a deep reinforcement learning network to map between environmental input and actions. Comparing to a supervised model of learning, we show that the reinforcement network closely matches aspects of human behavior in two experiments - learning of synthesized auditory noise tokens and improvement in speech sound discrimination. Both models perform comparably and the similarity in the output of each model leads us to believe that there is little inherent computational benefit to a reward-based learning mechanism. We suggest that the specific neural circuitry engaged by the paradigm and links between striatum and superior temporal areas play a critical role in effective learning.

The consequences of AI training on human decision-making

AI is now an integral part of everyday decision-making, assisting us in both routine and high-stakes choices. These AI models often learn from human behavior, assuming this training data is unbiased. However, we report five studies that show that people change their behavior to instill desired routines into AI, indicating this assumption is invalid. To show this behavioral shift, we recruited participants to play the ultimatum game, where they were asked to decide whether to accept proposals of monetary splits made by either other human participants or AI. Some participants were informed their choices would be used to train an AI proposer, while others did not receive this information. Across five experiments, we found that people modified their behavior to train AI to make fair proposals, regardless of whether they could directly benefit from the AI training. After completing this task once, participants were invited to complete this task again but were told their responses would not be used for AI training. People who had previously trained AI persisted with this behavioral shift, indicating that the new behavioral routine had become habitual. This work demonstrates that using human behavior as training data has more consequences than previously thought since it can engender AI to perpetuate human biases and cause people to form habits that deviate from how they would normally act. Therefore, this work underscores a problem for AI algorithms that aim to learn unbiased representations of human preferences.

Identification of psychological stress from speech signal using deep learning algorithm

Psychological activities have various dimensions in which they correlate with their respective behavior generated by the human body. Understanding the relationship of psychological events with the help of external action units is one of the research subjects to explore various human behavior and their dependencies. Existing work applied various deep learning algorithms to outline the correlation between psychological activities with human emotions. The study of psychological analysis in the medical field is very time-consuming and costly. It requires constant monitoring of the patient for a period of time and various interrogation sessions to finalize the emotional severity of an individual. Few skilled specialists and the lack of medical knowledge of human emotions drive the need for computer vision approaches to emphasize emotion recognition, particularly in disorders. The proposed study specifically assesses the use of speech signals to identify psychological disorders in terms of stress characteristics and uses a deep learning model that incorporates feed-forward networks and long short-term memory (LSTM) to identify the degree of psychological disease. The study made use of a standard speech dataset that was gathered from a variety of patients using a standard questionnaire format. The survey was conducted throughout a few Indian states. Speech samples were taken from patients whose cortisol levels were higher than 10 %. To assess the relationship between speech and psychological activity, speech signals from each patient have been gathered. The spectrogram of the speech signal's Mel filter bank coefficients has been analyzed, and the characteristics that cause stress and those that don't have it have been further divided into categories. The suggested model classifies stress and non-stress features in 150 voice dataset subjects with an average accuracy of 98 %. The model is found to be robust for various applications such as preventing suicidal cases, improving decision-making in the diagnosis of depression patients, improves the overall mental healthcare system.

Towards intelligent environments: human sensing through 3D point cloud

AbstractTo realize the concept of “Society 5.0”, which seeks to enhance well-being by integrating the Internet of Things to connect people, objects, and environments, intelligent environments are essential. These environments focus on creating sensor-enhanced spaces capable of recognizing and interpreting human behaviors and needs. This paper explores how we achieve intelligent environment by highlighting current technologies and applications of human sensing and tracking that enables such intelligent environments. Specifically, it introduces the development and application of LiDAR-based human sensing and tracking technologies, including the Hitonavi and Hitonavi-$$\mu $$ μ systems, designed for multi-person tracking in extensive areas and for enabling person detection and activity recognition on edge devices, respectively. Moreover, the paper explains the services and applications of these methodologies to smart buildings, shopping malls, and elderly care, showcasing their potential to enhance safety, convenience, and understanding of human behavior within intelligent environments. Throughout this paper, we present our vision for creating reliable intelligent environments, which are fundamentally supported by sophisticated human sensing technologies.

Video Salient Object Detection Via Multi-level Spatiotemporal Bidirectional Network Using Multi-scale Transfer Learning

Video saliency prediction aims to resemble human visual attention by identifying the most relevant and significant elements in a video frame or sequence. This task becomes notably intricate in scenarios characterized by dynamic elements such as rapid motion, occlusions, blur, background variations, and nonrigid deformations. Therefore, the inherent complexity of human visual attention behavior during dynamic scenes necessitates the assessment of both temporal and spatial data. Existing video saliency frameworks often falter under such conditions, and relying solely on image saliency models neglects crucial temporal information in videos. This study presents a new Video Salient Object Detection via Multi-level Spatiotemporal Bidirectional Network using Multi-scale Transfer Learning (MSB-Net) to address the problem of identifying significant objects in videos. The proposed MSB-Net achieves notable results for a given sequence of frames by employing multi-scale transfer learning with an encoder and decoder approach to acquire knowledge and saliency map attributes spatially and temporally. The proposed MSB-Net model has bidirectional LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) components. The VGG16 (Video Geometry Group) and VGG19 architectures extract multi-scale features from the input video frames. Evaluation of diverse datasets, namely DAVIS-T, SegTrack-V2, ViSal, VOS-T, and DAVSOD-T, demonstrates the model's effectiveness, outperforming other competitive models based on parameters such as MAE, F-measure, and S-measure.

The Prophetic Approach to Civilized Coexistence with the Other

This research aimed to know the concept of coexistence in Islam, especially in relation to the civil society built by the Prophet Muhammad, may God bless him and grant him peace, in Medina, which represents the model structure of civilizational coexistence with others. Moreover, it raises a set of questions about the concept of coexistence, the concept of the other, and the reality of coexistence in the Medina society (the society built by the Prophet, peace and blessings be upon him), in addition to the controls and forms of this coexistence. Therefore, the importance of this research is evident in clarifying modern concepts related to the relationship with others in civil society. This is done through the Prophetic applications in the Islamic community in Medina. The research clarifies the nature of relationships within the Prophet’s community and the forms of cultural relationship with others. Furthermore, it highlights the extent of our need for guidance and to follow the approach adopted by the Prophet, may God bless him and grant him peace, in civilizational coexistence with others. The research is based on the inductive analytical approach in examining the hadiths and Prophetic events that focus on clarifying the concept of coexistence with others, the controls and determinants of coexistence with others in the Prophetic civil society, and explaining the impact of coexistence on civil society. The results showed that the civil society founded by the Prophet, PBUH, in Medina was characterized by great civilizational principles that demonstrate justice, mercy, and good human behaviours. These principles reflect the need to highlight the Prophetic values of civilized coexistence that prevailed in civil society and how to deal with others in the current era, especially in light of the emergence and spread of ideologies of hatred and racism that have generated many negative convictions about the Islamic approach to coexistence with others.

Application of Video Abnormal Behavior Detection Algorithm in Evaluation of Track and Field Teaching and Training Effect

With the development of track and field, people pay more and more attention to the quality of classroom teaching of track and field technology, and the evaluation of teaching quality plays a key role in it. In today's educational reform, teaching evaluation plays an important role as an important method to test teachers' teaching and students' learning. With the rapid development of machine learning, especially deep learning, image-based individual abnormal behavior detection technology is becoming more and more mature, but there are still many difficulties to be solved in video-based group abnormal behavior detection technology. Therefore, it is necessary to study the detection algorithm. Based on machine vision theory, image processing theory and video analysis technology, this paper studies three key technologies involved in human abnormal behavior detection in video: moving target detection, moving target tracking and abnormal behavior detection.

Running away from the marshmallow: the relevance of behaviour settings for a situated science of self-control.

The behaviour settings approach was introduced as a means to study the variability of human beings' behaviour outside the lab. More recently, it has been argued that it also provides a fruitful avenue for developing situated accounts of cognition. This article will provide a proof of concept for the latter suggestion, focusing on the science of self-control. Self-control is the ability of individuals to pursue goals they value in the face of conflicting motivations. The hypothesis we bring forward is that this ability should be understood as a set of skills by which individuals modulate their relation to their environment, more specifically the behaviour settings they inhabit. With this conception of self-control in hand, we will take a critical look at well-known experiments involving delayed gratification tasks and propose concrete suggestions on how to improve them. This will bring us to the conclusion that the behaviour settings framework might have a valuable role to play in developing a situated science of self-control. This article is part of the theme issue 'People, places, things and communities: expanding behaviour settings theory in the twenty-first century'.

Impacts of obesity and obesogenic behaviours on climate change: a systematic review protocol

Background Unbalanced diet and sedentary habits, defined as obesogenic behaviours, are continuously rising in our society, posing serious threats to public health. Indeed, this lifestyle exacerbates pathological conditions, including obesity, metabolic syndrome, cardiovascular risk and cancer. Climate change, increasing temperatures and extreme weather exacerbate obesogenic behaviours. Climate and environment strongly influence lifestyles and can in some way promote obesogenic behaviour. However, little is known about the effects of obesogenic behaviours/obesity on climate change. To address this relation, we planned a systematic review with eventual meta-analysis. Methods Terms related to obesogenic behaviours and climate change will be used to develop the search algorithm which will be applied to four databases: PubMed, Scopus, Web of Science and EBSCO. Title and abstracts will be screened, and two reviewers will review article full-text. The risk of bias of the eligible publications will be assessed using the Office of Health Assessment and Translation (OHAT) tool. Results will be summarized narratively and if possible, meta-analysis will be performed. Discussion Obesogenic behaviours are recognized threats to public health. Instead, the effects that these habits could have on climate change are less recognized and, yet to date, no systematic review has addressed this relation. In this study we will consider the data currently available to deepen our knowledge about the role of human behaviours on greenhouse gases and climate change, and possibly identify strengths on which we can act. The identification of new strategies and guidelines could have a beneficial impact, on a short term, on public health and, on a long term, would mitigate climate changes, that in turn would reduce the incidence of obesity. Registration In accordance with PRISMA guidelines, our systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on 29 October 2023 (registration number CRD42023473107).

The Concept of Indonesian Character Education: Emile Durkheim's Perspective

The aim of preparing this work is to analyze in detail the concept of Indonesian character education from Emile Durkheim's perspective. In researching the concept of character education from Emile Durkheim's perspective, the initial step is to identify relevant data sources, including Durkheim's work and academic analysis of his views. Then, the researcher conducted a critical study of the selected literature, evaluating the main themes and key concepts related to character education from Durkheim's perspective. Next, through qualitative analysis, they compared Durkheim's views with contemporary understandings of character education to conclude the relevance and applicability of Durkheim's concepts in today's educational context. Durkheim's contribution to sociology was to introduce the concepts of social facts and solidarity, highlighting the importance of understanding the social forces that shape human behavior and the relationship between individuals and society in modern social dynamics. With his empirical approach, Durkheim built a fundamental foundation for the study of social interaction and community formation. Character education in Indonesia, supported by government policies and related ministries, emphasizes cultural values and virtues in developing students' hearts, minds, and bodies, as well as reason and creativity, with an emphasis on honesty, intelligence, resilience, and empathy as the main values. The main goal is to form a generation that is not only academically intelligent but also morally responsible and appreciates the diversity of society. Character education in Indonesia reflects conformity with Emile Durkheim's view of the influence of society on individual behavior through an emphasis on communal values, strengthening solidarity in society.

A "Self-Milieux" perspective on help-seeking: examining the impact of a person's sociocultural background on help-seeking in people with untreated depressive symptoms.

Mental illness is a global concern and the leading cause of years lived with disability. Research on help-seeking behaviour has focused on individual factors, but there is still much unexplained variance. Suggesting complex interactions between determinants of human behaviour a new framework called Self-Milieux is proposed to represent a person's sociocultural background. The article introduces a statistical approach to determine Self-Milieux and exemplarily examines its predictive validity for health-related research. Self-Milieux are determined through a two-stage clustering method based on the determinants socioeconomic status and self-construal profile. Descriptive analyses are used to compare Self-Milieux characteristics. Hierarchical binary logistic regression models test the association between Self-Milieux and help-seeking behaviour, while controlling for socioeconomic status as an established predictor. The sample size was N = 1535 (Mage = 43.17 and 64.89% female participants). Average depression severity was M = 12.22, indicating mild to moderate symptoms. Six Self-Milieux were determined and named. Participants from privileged (aOR = 0.38) and self-sufficient (aOR = 0.37) milieux were less likely to seek help from a general practitioner than those from the entitled milieu. Participants from privileged (aOR = 0.30), collaborators (aOR = 0.50), disadvantaged (aOR = 0.33), and self-sufficient (aOR = 0.21) milieux were less likely to seek help from family members than those from the entitled and family-bound milieux. The study's strengths and limitations, as well as the cluster methodology, are discussed. The comparative results for the six Self-Milieux are interpreted based on current research. For example, participants from some milieux follow a help-seeking process proposed in previous research, while participants from other milieux seem to show a different process, one that ends in informal help-seeking.

Childhood-Onset Fluency Disorder with marked ingressive speech: a prospective case study

This prospective case study aimed to examine the effectiveness of a comprehensive treatment approach employed for a 14-year-old boy, EK, with Childhood-Onset Fluency Disorder (COFD), particularly manifested by excessive pulmonic ingressive speech and phonation as well as profound secondary behaviors that resemble those attested in individuals with Tic Syndrome (TS). A single-case, experimental-longitudinal design was used, with a hypothesis adopting a therapeutic approach, which integrated strategies used to manage several behaviors in TS, would reduce dysfluency and associated secondary behaviors and that gains would be maintained. Perceptual and instrumental data were obtained. The findings offered evidence for the effectiveness of the therapeutic program, which could be attributed to the possible similarities in the underlying pathogenesis of stuttering and TS – glitches in the neurophysiological system that give rise or contribute to the two disorders. The results further back up comprehensive assessment and therapeutic approach facilitating better understanding and management of complex human behaviors like stuttering.

Prolonged unemployment is associated with control loss and personal as well as social disengagement.

The need for control is a fundamental human motivation, that when deprived can lead to broad and substantial changes in human behavior. We aimed to assess the consequences of control deprivation in a real-life situation that poses a severe threat to personal control: a prolonged unemployment. Using a sample N = 1055 of unemployed (n = 748) versus working (n = 307) individuals, we examined predictions derived from two models of reactions to control deprivation: control-regaining and disengagement/withdrawal. We found that length unemployment is correlated with a psychological state strongly interfering with psychological as well as social functioning. While control-regaining models of responding to lack of control have received virtually no support from our findings, our results provide evidence that long-term unemployed individuals are more disengaged than working individuals. They are more apathetic, less likely to engage in control-regaining efforts and in active forms of construing one's own future.

The extended Grossman human capital model with endogenous demand for knowledge

This paper extends the Grossman human capital model by including endogenous demand for knowledge capital. Human capital investment behavior is modeled from the start of a person's life rather than only after she has acquired the optimal stock of knowledge capital or reached the mandatory schooling age. The general condition for the optimal knowledge capital stock and the optimal health capital stock is derived, and the conditions for an individual's choices of interior and corner solutions for learning time that increases knowledge capital stock are presented. The extended Grossman human capital model provides a theoretical basis for explaining and predicting the effects of economic conditions on educational attainment from a distinctive perspective based on the joint determination of health capital and knowledge capital.

Observational study of close contact behaviors in a multinational graduate student office

Understanding human contact behavior plays a crucial role in improving the design of indoor environments, for example, by enhancing thermal comfort, workplace performance and productivity, and controlling infection transmission. Although baseline information for indoor environmental design is available, data on person-to-person contact behavior are limited, especially in populations of diverse nationalities, genders, and age ranges. In this study, we used high-resolution video cameras to collect close contact data for five consecutive school days in a multinational graduate school student office in Japan with a reserved cultural background. We characterized human close-contact behavior using stochastic interactions in terms of interpersonal distance, assortativity, posture, duration, and frequency of contact in a multinational population. Our results showed a remarkable difference compared to the study results from person-to-person contact behavior in an ethnically homogenous population. The interpersonal distance averaged 0.75 m, and the close contact frequency was notably lower, but the close contact duration was significantly longer. Nationality showed stronger homophily. We considered the degree centrality measures of the nodes in the observed networks. The observed contact network links were dense with high reciprocity. High contact heterogeneity was observed in twenty-five percent of the nodes. These data can be used to improve targeted epidemiological analyses for indoor occupant health decision-making, and infection spread prevention strategies.

Human-in-the-Loop Behavior Modeling via an Integral Concurrent Adaptive Inverse Reinforcement Learning.

One goal of artificial intelligence (AI) research is to teach machines how to learn from humans, such that they can perform a certain task in a natural human-like way. In this article, an online adaptive inverse reinforcement learning (IRL) approach to human behavior modeling is proposed to enhance machine intelligence for a class of linear human-in-the-loop (HiTL) systems using the state data only, where the human behavior is described by a linear quadratic optimal control model with an unknown weighting matrix for the quadratic cost function. First, an integral concurrent adaptive law is developed to learn the human feedback gain matrix online using the demonstrated state data only, which removes the persistent excitation (PE) conditions required by traditional adaptive estimation approaches and thus is more in line with real applications. Then, with the learned feedback gain matrix, the IRL problem is formulated as a linear matrix inequality (LMI) optimization problem, which can be efficiently solved to retrieve the weighting matrix of the human cost function. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approach.

A system for monitoring human postures, seizures, and falls from bed using radio and surface electromyography signals

In this work, a system for monitoring human postures, seizures, and falls from bed using received signal strength indicator (RSSI) and surface electromyography (sEMG) signals is studied through experiments. In this proposed system, a person who is located inside a wireless link is monitored by considering the change in measured RSSI signals as the 2.4 GHz IEEE 802.15.4 signals received at a receiver. Human motions in bed that affect RSSI levels can be captured. Thus, with this technique, it does not raise a privacy concern compared with vision-based technology. Additionally, sEMG signals associated with muscle movements from human postures are recorded from the human body’s abdominal muscles. Eight different activities, including normal and critical events, are tested and evaluated. Experimental results indicate that the proposed system could automatically monitor different human postures in real-time. RSSI and sEMG signals correlated to each posture have their own patterns. Furthermore, the relationship between human behaviors and RSSI and sEMG levels is summarized.

Malaria burden and residual transmission: two thirds of mosquito bites may not be preventable with current vector control tools on Bioko Island, Equatorial Guinea

This study assesses exposure to malaria vector mosquitos that is non-preventable through use of nets, the contribution of outdoor and indoor biting towards residual vector exposure, and the risk factors for being bitten and for being infected with malaria parasites on Bioko Island, Equatorial Guinea. Human behaviour and malaria infection data were collected from 13,735 randomly selected residents during cross-sectional surveys, concomitantly with entomological human landing catches, indoors and outdoors, in 20 locations on the Island. Self-reported time of going indoors, going to bed and whether using a net were analysed to impute for each respondent the number of bites received outdoors and indoors during the night before the survey. On average, each person received 2.7 (95% CI 2.6 to 2.8) bites per night outdoors, 8.5 (8.3 to 8.7) bites indoors if not using a net, and 4.7 (4.5 to 4.8) bites indoors if using a net. Malaria infection was associated with more bites, regardless of whether received indoors or outdoors. Older age, male gender, not using a net, rural location and going indoors later increased the risk of being bitten. The proportion of bites not averted by using a net was estimated as 66% (61 to 71). A large proportion of biting, mostly indoors, may not be preventable by LLINs. Tools targeting indoor biting should be prioritised in Bioko. Novel vector control tools are urgently needed to reduce overall exposure to mosquito bites.

Barriers and enablers for the adoption of sustainable design practices using new design methods – Accelerating the sustainability transformation in the manufacturing industry

Product development and manufacturing organizations struggle in their sustainability transformation and do not sufficiently contribute to sustainable production and consumption. Design researchers, at the same time, develop and propose a plethora of new and improved design methods that can support the manufacturing industry in such transformation. It is, despite this, well-documented in literature that the industrial adoption of such proposed design methods is challenging. Previous research in the design domain has mainly studied this issue from a process and methodological perspective, whereas previous research in the management domain instead has focused on organizational, and human-behavioral aspects. This poses a research gap for more interdisciplinary research that studies the adoption of design methods from all three perspectives (i.e., process and methodology, organization, and human behavior). Six parallel case studies were carried out with three different product development and manufacturing organizations to collect qualitative empirical data. Glaserian grounded theory was used to analyze the collected data. This resulted in a descriptive framework that captures 53 interdisciplinary factors influencing the adoption of sustainable design practices using new and improved design methods. The descriptive framework is compared to interdisciplinary literature to further clarify and explain the findings, highlighting both practical and theoretical implications. This research provides three main contributions to theory and practice: (1) Two new concepts are introduced and used to explain the empirical findings, which are referred to as the dualism of design methods, and the situational design problem; (2) Nine systemic barriers and eight propositions are formulated, which highlight the need for a paradigm shift in how design is practiced in industry, how cognitive biases inside organizations can lead to a state of pseudo-sustainability, and the need for improved information and data management capabilities in organizations; (3) Sustainable design thinking is proposed as an potential enabler to address several of the main barriers, as it aims to provide a base competence of sustainable design to systematically challenge cognitive biases inside organizations.

Ubiquitous computation in internet of vehicles for human-centric transport systems

The Internet of Vehicles (IoV) has the potential to bring about a revolutionary transformation in transportation through its influence on human behavior and interactions between users and vehicles. However, interoperability challenges between retailer organizations and manufacturers present a barrier to decision-making processes and impact the human-centric nature of the IoV. Ethical dilemmas arise as a result of the IoV’s inability to prevent accidents, particularly in critical situations. This study aims to enhance the IoV’s effectiveness by carefully selecting and improving essential attributes from various data sources, including sensors, GPS, 5G or 6G communication networks, and real-time data provisioning. To achieve the aim of the proposed study, a Multi-criterion Decision-making (MCDM) approach is proposed, which allows for the analysis and selection of optimal choices while taking into account various quantitative and qualitative factors. Despite the challenges posed by complex models and ambiguous data, MCDM remains an indispensable technique for aligning transportation systems with current expectations. The CRITIC and TOPSIS MCDM-enabled methodologies are employed to analyze IoV architecture, prioritizing significant elements that impact system performance and identifying optimal solutions by considering complications from worst-case scenarios. The study will assist engineers, scientists, and organizations to develop smart IoV systems that will cater to human needs by improving mobility and inspiration among users.