Behavioral Cues Research Articles

“Context and Safety Are Everything”: Exploring How Pansexual Individuals Mark and Express Their Identity

Sexuality marking serves to assert one’s sexuality to others. This can be done through language, behavior, esthetics, and/or other non-verbal cues (Morgan & Davis-Delano, 2016). This research explored how individuals who identify as pansexual mark their sexuality within different contexts. An online qualitative survey was completed by 45 participants aged between 18 and 58. Thematic analysis revealed 3 key themes: ‘You just don’t want to risk it’: The importance of safety on sexuality marking, ‘My sexuality wasn’t real’: Dismissal and stereotyping of the pansexual identity within intimate partner relationships, and ‘I’m very loud and proud’. This research is understood to be the first study on sexuality marking among the pansexual community and advances the understanding of the ways in which pansexual individuals navigate and express their identity.

Retrosplenial inputs drive visual representations in the medial entorhinal cortex

The importance of visual cues for navigation and goal-directed behavior is well established, although the neural mechanisms supporting sensory representations in navigational circuits are largely unknown. Navigation is fundamentally dependent on the medial entorhinal cortex (MEC), which receives direct projections from neocortical visual areas, including the retrosplenial cortex (RSC). Here, we perform high-density recordings of MEC neurons in awake, head-fixed mice presented with simple visual stimuli and assess the dynamics of sensory-evoked activity. We find that a large fraction of neurons exhibit robust responses to visual input. Visually responsive cells are located primarily in layer 3 of the dorsal MEC and can be separated into subgroups based on functional and molecular properties. Furthermore, optogenetic suppression of RSC afferents within the MEC strongly reduces visual responses. Overall, our results demonstrate that the MEC can encode simple visual cues in the environment that may contribute to neural representations of location necessary for accurate navigation.

Popularity at first sight: Dominant behaviours mediate the link between extraversion and popularity in face-to-face and virtual group interactions.

Although there is robust evidence that being more extraverted is related to higher popularity, only few studies have examined which actual behaviours (e.g., verbal content, body language) might explain this association. The current study examined whether observer-rated dominant behaviours (nonverbal, paraverbal, verbal, and general cues) mediate the relationship between self-rated extraversion and its facets (assertiveness, sociability, and activity) and other-rated popularity in zero-acquaintance settings. In two studies, we analysed data from face-to-face (Study 1, N = 124) and virtual (Study 2, N = 291) group interactions where participants were videotaped while performing a task and subsequently rated each other on popularity. Across studies, extraversion and the facets assertiveness and sociability were consistently associated with higher popularity, while the role of dominant behaviours differed. In Study 1, only two nonverbal behaviours, dominant gestures and upright posture, mediated the association between extraversion and popularity. In Study 2, all four types of behavioural cues mediated the association between extraversion (facets) and popularity. We discuss how these findings provide insights into the mechanisms of attaining popularity at zero acquaintance in diverse social settings.

Chemical cues in the mating behavior of a highly polygynous bird

Chemical cues in the mating behavior of a highly polygynous bird

Revolutionizing Identity Verification and Recognition

The innovative integration of real-time human detection improves both security and user experience in identity authentication. This approach improves authentication processes by analysing physiological and behavioural indicators such as face movements and eye movements, hence reducing fraud risks. Its deployment across sectors promises to enhance security in finance, healthcare, e-commerce, and law enforcement. Despite the hurdles, the benefits of this human-centered approach are enormous, indicating a more secure digital future. Keywords— Identity verification, live human detection, digital security, biometric authentication, user experience, fraud prevention, computer vision, physiological and behavioural cues, facial and vocal patterns, finance, healthcare, e-commerce, law enforcement.

Drowsiness Detection and Alert System

Drowsiness detection is a solution for identifying signs of fatigue or sleepiness in individuals. One of the key features of our model is that it can detect drowsiness at night as well using Mobile cameras (infrared sensors). The system captures infrared images of the person's face and analyzes the physiological and behavioral cues related to drowsiness. Infrared sensors allow for drowsiness detection in low-light conditions, making it particularly useful for night-time scenarios such as night driving. The system can trigger alerts or interventions if drowsiness is detected, helping to prevent accidents or mistakes. We will be using libraries like OpenCV, TensorFlow, CNN, and VGG19 features in our model. By combining the accessibility of Android devices with the advanced capabilities of the Deep Learning algorithm, drowsiness detection using infrared sensors has the potential to greatly improve the safety and productivity of individuals in their daily lives.

Chinese undergraduates' mental health help-seeking behavior: the health belief model.

The detection rate of mental health problems among undergraduates has recently risen significantly. However, undergraduates underutilize mental health services; approximately a third only of undergraduates in need of treatment use school counseling resources. Based on a social psychological theoretical framework, the health belief model, factors of undergraduates' willingness to seek help when dealing with psychological problems were investigated. A cross-sectional online questionnaire and a snowball sampling method with 446 undergraduates investigated perceived susceptibility, perceived severity, perceived behavioral benefits, perceived barriers, self-efficacy, and cues to action to understand how students' mental health-seeking behaviors are affected. We found that perceived susceptibility (p < 0.01), perceived severity (p < 0.01), perceived benefits (p < 0.01), perceived barriers (p < 0.01), self-efficacy (p < 0.01), and cues to action (p < 0.01) significantly correlated with behavioral intention. Encouragement or counseling from others would be more likely to motivate undergraduates to seek mental health help. In addition, we used a bias-corrected Bootstrap approach to test the significance of the mediating effect, the mediation effect of cues to action between undergraduates' perceived susceptibility and mental health help-seeking behavior was utterly significant [mediation effect value of 0.077, with an SE value of 0.027 and a 95% CI (0.028, 0.133)]. It demonstrated that those who perceived themselves to be at high risk of developing a mental illness and who had received encouragement or counseling to seek mental health help were more likely to be motivated to seek mental health help. Multiple regression analyses indicated that self-efficacy (Z = 5.425, p < 0.01) and cues to action (Z = 6.673, p < 0.01) independently influenced behavioral intentions. Encouragement or counseling from others would be more likely to motivate undergraduates to seek mental health help.

Observing Interviewees’ Inner Self: How Authenticity Cues in Job Interviews Relate to Interview and Job Performance

AbstractJob interviews are among the most popular selection methods. Previous research suggests that interviewees who are perceived as being authentic are evaluated more favorably in job interviews. However, little is known about which behavioral cues elicit perceptions of authenticity in others and whether interviewees who exhibit such authenticity cues are more likely to perform better in the job interview and on their actual job. Drawing from person perception theories, the purpose of this study is to introduce the concept of authenticity cues to interview research and to examine the extent to which authenticity cues are related to (a) raters’ perceptions of interviewee authenticity, (b) interviewer ratings of interview performance, and (c) supervisor ratings of job performance. We used video recordings from 181 employed individuals participating in a mock interview to observe and rate interviewees’ authenticity cues. Results indicate that observers can distinguish between verbal and para/nonverbal authenticity cues and that both verbal and para/nonverbal cues influence the extent to which interviewees are perceived as authentic by independent raters. Moreover, we found that interviewees’ para/nonverbal authenticity cues were particularly relevant to predicting interview performance, whereas only verbal authenticity cues were related to job performance. In our analyses, we further considered the role of first impressions in the interview, interviewees’ verbal cognitive ability, and interviewees’ extraversion. Implications for theories of person perception, authenticity research, and interview practice are discussed.

ANIMAL GAZING DETECTION USING CNN IN SMART AGRICULTURAL FIELD

Animal detection and warning systems play a critical role in safeguarding agricultural land from the intrusion of large animals, particularly ungulates, which pose threats to property and human safety. These systems are designed to identify and alert the presence of such animals before they encroach upon farming territories. Across different regions, the deployment of these systems aims to mitigate Animal Vehicle Collisions (AVCs) involving ungulate species, which can result in significant costs and hazards. However, the effectiveness of these systems may be hindered by the lack of regular monitoring for incidents of animal road fatalities. This paper explores the operational principles of animal detection systems, highlighting their reliance on machine learning technology to detect patterns indicative of animal presence. The detection process involves analyzing data collected from various sources to discern behavioral and environmental cues associated with animal activity. Challenges may arise from accurately distinguishing between different types of animals and background elements, necessitating the refinement of machine learning algorithms to minimize false positives triggered by environmental factors. Effective deployment of these systems requires comprehensive planning and integration into regional and national agricultural policies. By leveraging advancements in machine learning technology and data analytics, stakeholders can enhance the efficiency of these systems, thereby reducing the incidence of animal- related accidents and protecting agricultural resources. This paper underscores the importance of continuous monitoring and adaptation of animal detection systems to effectively mitigate risks and ensure the sustained protection of farming land and communities.

Don't Flip the Table Yet: A Framework for the Analysis of Visual and Cognitive Ergonomics in Board Games

Board games provide immersive and enjoyable experiences for players, but they can also pose usability challenges. This article presents a comprehensive framework for analyzing board game design, focusing on identifying the elements that make play inadvertently difficult or fatiguing. The proposed framework analyses board games as a user interface between the players and the board game mechanics, thereby focusing on the presentation aspects without altering the underlying rules. The intent is to preserve the deliberate design challenges that enhance gameplay while pinpointing the elements that affect the game experience negatively. We employ a methodology that begins with the observation of players’ behavioral cues to infer internal states such as cognitive or visual fatigue. These inferred states serve as a basis to identify problematic features in the game components or presentation. We illustrate the framework through a series of case studies, providing practical examples of the analysis process through an idiographic approach.

Learning in the workplace: Development of a simple language statement assessment tool that supports second-level nurse practice

AimTo focus learning through clarity of the enrolled nurse (EN) role (a second tier nurse position) through development of a user-friendly workplace performance assessment tool commensurate with EN standards for practice. BackgroundInternationally, the nursing workforce comprises regulated and unregulated staff. In Australia, similar to other western countries, there are two tiers of regulated workforce, namely Registered Nurses (RNs) and Enrolled Nurses (ENs). Differences in RN and EN standards based on the education preparation are not always clearly differentiated in workplace practice. Roles are often seen as interchangeable: Improved clarity of both regulated and unregulated roles, when numbers of healthcare workers are burgeoning, assists performance assessment that guides further learning and safe practice. DesignTwo phase sequential, non-experimental design. MethodsPhase one used focus groups (n=48), expert reference panel (n=8) and end-users (n=16) to develop simple language statements. Phase two involved field testing of the statements. FindingsA 30-item, criterion-based workplace performance tool was developed. Principal component analysis of completed tools indicated work could be organised around three key areas of practice, namely, higher order thinking and problem solving, routine daily activities of care and personal and social attributes. DiscussionParticipants reported the statement items assisted in determining suitable activities and accompanying cues in discussing learning needs. Analysis assisted with discriminating broader elements of EN workplace performance. ConclusionsWorkplace learning is important for nurses to continue to build their capacity to deliver optimum care. Assessment tools that describe professional capability in plain language statements and provide examples of supportive behavioural cues help guide on-going learning through improving the validity and thereby consistency of assessment processes. Furthermore, comprehensible and meaningful statements and cues can readily be adopted by students and educators to target learning and feedback thereby enhancing clarity of the EN role, to distinguish from other nursing roles.

Objective measure of binaural processing: Acoustic change complex in response to interaural phase differences

Combining cochlear implants with binaural acoustic hearing via preserved hearing in the implanted ear(s) is commonly referred to as combined electric and acoustic stimulation (EAS). EAS fittings can provide patients with significant benefit for speech recognition in complex noise, perceived listening difficulty, and horizontal-plane localization as compared to traditional bimodal hearing conditions with contralateral and monaural acoustic hearing. However, EAS benefit varies across patients and the degree of benefit is not reliably related to the underlying audiogram. Previous research has indicated that EAS benefit for speech recognition in complex listening scenarios and localization is significantly correlated with the patients’ binaural cue sensitivity, namely interaural time differences (ITD). In the context of pure tones, interaural phase differences (IPD) and ITD can be understood as two perspectives on the same phenomenon. Through simple mathematical conversion, one can be transformed into the other, illustrating their inherent interrelation for spatial hearing abilities. However, assessing binaural cue sensitivity is not part of a clinical assessment battery as psychophysical tasks are time consuming, require training to achieve performance asymptote, and specialized programming and software all of which render this clinically unfeasible. In this study, we investigated the possibility of using an objective measure of binaural cue sensitivity by the acoustic change complex (ACC) via imposition of an IPD of varying degrees at stimulus midpoint. Ten adult listeners with normal hearing were assessed on tasks of behavioral and objective binaural cue sensitivity for carrier frequencies of 250 and 1000 Hz. Results suggest that 1) ACC amplitude increases with IPD; 2) ACC-based IPD sensitivity for 250 Hz is significantly correlated with behavioral ITD sensitivity; 3) Participants were more sensitive to IPDs at 250 Hz as compared to 1000 Hz. Thus, this objective measure of IPD sensitivity may hold clinical application for pre- and post-operative assessment for individuals meeting candidacy indications for cochlear implantation with low-frequency acoustic hearing preservation as this relatively quick and objective measure may provide clinicians with information identifying patients most likely to derive benefit from EAS technology.

Gaze-based intention estimation: principles, methodologies, and applications in HRI

Intention prediction has become a relevant field of research in Human-Machine and Human-Robot Interaction. Indeed, any artificial system (co)-operating with and along humans, designed to assist and coordinate its actions with a human partner, would benefit from first inferring the human’s current intention. To spare the user the cognitive burden of explicitly uttering their goals, this inference relies mostly on behavioral cues deemed indicative of the current action. It has been long known that eye movements are highly anticipatory of the single steps unfolding during a task, hence they can serve as a very early and reliable behavioural cue for intention recognition. This conceptual review aims to draw a line between insights in the psychological literature on visuomotor control and relevant applications of gaze-based intention recognition in technical domains, with a focus on teleoperated and assistive robotic systems. Starting from the cognitive principles underlying the relationship between intentions, eye movements, and action, the use of eye tracking and gaze-based models for intent recognition in Human-Robot Interaction is considered, with prevalent methodologies and their diverse applications. Finally, special consideration is given to relevant human factors issues and current limitations to be factored in when designing such systems.

Deciding Who Is Worthy of Help: Effect of the Probability of Reciprocity on Individuals' Willingness to Help Others.

In order to explain helping strangers in need in terms of reciprocal altruism, it is necessary to ensure that the help is reciprocated and that the costs of helping are thus compensated. Competence and willingness to make sacrifices for the benefactor of the person being helped are important cues for ensuring a return on help because reciprocity would not be possible if the person being helped had neither the competence nor the inclination to give back in the future. In this study, we used vignettes and manipulated the cause of suffering strangers' difficulties and prosociality to investigate participants' compassion for and willingness to help the stranger. In Study 1, we measured willingness to help by using hypothetical helping behaviors that were designed to vary in cost. In Study 2, we measured willingness to help by using the checkbox method in which participants were asked to sequentially check 10 × 10 checkboxes on a webpage, which asked the participants to pay a small but real cost. In both studies, the controllability of the cause and the prosociality were found to independently affect compassion. These two factors also independently affected willingness to help, as measured by both the hypothetical questions and the checkbox method. We consequently discussed the reasons for the independent processing of the competence and behavioral tendency cues.

Risk and prosocial behavioural cues elicit human-like response patterns from AI chatbots

Emotions, long deemed a distinctly human characteristic, guide a repertoire of behaviors, e.g., promoting risk-aversion under negative emotional states or generosity under positive ones. The question of whether Artificial Intelligence (AI) can possess emotions remains elusive, chiefly due to the absence of an operationalized consensus on what constitutes 'emotion' within AI. Adopting a pragmatic approach, this study investigated the response patterns of AI chatbots—specifically, large language models (LLMs)—to various emotional primes. We engaged AI chatbots as one would human participants, presenting scenarios designed to elicit positive, negative, or neutral emotional states. Multiple accounts of OpenAI's ChatGPT Plus were then tasked with responding to inquiries concerning investment decisions and prosocial behaviors. Our analysis revealed that ChatGPT-4 bots, when primed with positive, negative, or neutral emotions, exhibited distinct response patterns in both risk-taking and prosocial decisions, a phenomenon less evident in the ChatGPT-3.5 iterations. This observation suggests an enhanced capacity for modulating responses based on emotional cues in more advanced LLMs. While these findings do not suggest the presence of emotions in AI, they underline the feasibility of swaying AI responses by leveraging emotional indicators.

Semantic learning and attention dynamics for behavioral classification in police narratives

The proactive identification of behavioral health incidents concerns from police reports is a critical yet underexplored area. The law enforcement officers provide follow-up services to improve community life by manually analyzing and identifying generated public narrative reports after the 911 incident calls. Therefore, automatically identifying these behavioral health calls from public narrative reports helps reduce the current manual labor-intensive identification process for law enforcement officers. In this work, we introduce a novel, multi-faceted approach that combines manual expert annotations, natural language processing (NLP), and cutting-edge machine learning strategies to classify and understand these incidents within police narratives efficiently. Our proposed method retrieves relevant narrative reports utilizing domain knowledge as behavioral health cues as terms/keywords. Our approach automatically extracts different categories of behavioral health cues by utilizing the limited domain knowledge enabled behavioral health terms using a cosine similarity-based thresholding approach. The behavioral health classification model employs an automatic attention-aware feature representation of terms/keywords, categories, and narrative reports to identify behavioral health cases with an accuracy of 85%. Extensive evaluation shows that our proposed model outperforms all the state-of-the-art models by approximately 4%.

Adaptive attention-aware fusion for human-in-the-loop behavioral health detection

Identifying behavioral health is paramount for law enforcement officers to provide appropriate follow-up community care. In the current practice, law enforcement offices manually identify these behavioral health cases to allow the designation of the relevant follow-up resources. In this work, we develop a tool to automatically detect behavioral health cases from police public narrative reports by identifying behavioral health indicator signals. We propose a novel adaptive attention-aware fusion model for detecting behavioral health signals in sensitive police reports. Our model leverages contextual and semantic information from the reports and relevant behavioral health cues as keywords from a pre-trained attention-weighted keyword-based model. Our model also employs label self-attention mechanisms to correlate label embeddings with the report and keyword representations. Furthermore, we propose a novel clustering-based uncertainty-enabled informative sampling query strategy to integrate humans-in-the-loop in the active learning framework to reduce required annotation from experts. This querying strategy selects the most informative and diverse samples for expert annotation. Our experimental results showed that the proposed model outperforms state-of-the-art classifiers on a dataset of 300 manually annotated ground truth police reports, achieving an accuracy of 87.58% and an F1-score of 85.67%. Applying our querying strategy to our proposed model increased the detection of behavioral health, achieving an accuracy of 92% and an F1-score of 91.1%. Also, our proposed model achieves an accuracy score of 93.75% and an F1-score of 93.61% on unseen samples. Lastly, our proposed model demonstrates its interpretability by extracting the keywords associated with each behavioral health category.

The Grammar of Persuasion: A Meta-Analytic Review Disconfirming the Role of Nouns as Linguistic Cues of Subsequent Behavior

Recent discoveries suggest that nouns, as a word class, exhibit a greater capacity to model real-life behavior. In some previous studies participants prompted with a noun label rather than a semantically corresponding verb ( be a voter vs. vote) were more likely to vote. However, many attempts to replicate the original results have shown unsuccessful. To shed light on the effectiveness of subtle linguistic manipulations, we conducted a meta-analysis of 28 effect sizes ( N = 13,376), being an exact or conceptual replications of original studies. Although the average effect was significant ( g = 0.12), the concurrent diagnostics indicated a low trust in the replicability or stability of the effect. Based on that, we cannot conclude that the greater effectiveness of nouns as cues has been reliably demonstrated. We discuss theoretical prospects involving the verb-action link as well as the self-verification motives.

Obstetrics and gynecology applicant perceptions of residency program culture with virtual interviews: a qualitative analysis of social media posts

BackgroundIn the United States, Obstetrics and Gynecology residency interviews are instrumental in assessing the compatibility between medical student applicants and residency programs during the match process. Applicant perceptions of Obstetrics and Gynecology residency culture are a key component in determining how they rank residency programs. In 2020, residency interviews transitioned to a virtual format, and little is known about how applicants evaluated program culture during this first round of universal virtual interviews. Medical students in the United States commonly use Reddit, a popular social media platform, to discuss residency programs and share interview experiences. We explored Obstetrics and Gynecology applicants’ considerations regarding residency program culture during the first universal virtual interview season in 2020–2021 by analyzing posts on a Google spreadsheet accessed through Reddit.MethodsIn 2022, we imported 731 posts from the “2020-21 OB GYN Residency Applicant Spreadsheet” Google spreadsheet posted to the 2020–2021 Residency Interview Spreadsheet megathread on the r/medicalschool subreddit to NVivo 12(QSR International, Burlington, MA), a qualitative analysis software program. Three investigators used qualitative inductive techniques to code and identify themes.ResultsApplicants used visual, verbal and behavioral cues during virtual Obstetrics and Gynecology residency interviews to understand three components of the workplace culture: prioritization of diversity, equity and inclusion, social environment, and resident workload.ConclusionsObstetrics and Gynecology residency programs convey information about their culture during virtual interviews through the behavior, appearances and responses of residents and interviewers to applicant questions. To ensure they accurately represent their culture to applicants, programs should consider educating residents and faculty around the implications of interview-day conduct.

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.