The Face of Social Networks: Naive Observers’ Accurate Assessment of Others’ Social Network Positions From Faces

2021 ◽  
pp. 194855062110037
Author(s):  
Nicholas P. Alt ◽  
Carolyn Parkinson ◽  
Adam M. Kleinbaum ◽  
Kerri L. Johnson
Keyword(s):  
Social Network ◽  
Professional School ◽  
Actual Number ◽  
School Students ◽  
Social Network Data ◽  
The Face ◽  
Facial Images ◽  
Trait Impressions ◽  
Social Network Position ◽  
Zero Acquaintance

We examined whether, even at zero acquaintance, observers accurately infer others’ social network positions—specifically, the number and patterning of social ties (e.g., brokerage—the extent to which a person bridges disconnected people) and the trait impressions that support this accuracy. We paired social network data ( n = 272 professional school students), with naive observers’ ( n = 301 undergraduates) judgments of facial images of each person within the network. Results revealed that observers’ judgments of targets’ number of friends were predicted by the actual number of people who considered the target a friend (in-degree centrality) and that perceived brokerage was significantly predicted by targets’ actual brokerage. Lens models revealed that targets’ perceived attractiveness, dominance, warmth, competence, and trustworthiness supported this accuracy, with attractiveness and warmth most associated with perceptions of popularity and brokerage. Overall, we demonstrate accuracy in naive observers’ judgments of social network position and the trait impressions supporting these inferences.

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

Meditation buffers medical student compassion from the deleterious effects of depression.

2017 ◽  
Author(s):  
Jennifer S Mascaro ◽  
Sean Kelley ◽  
Alana Darcher ◽  
Lobsang Negi ◽  
Carol Worthman ◽  
...  
Keyword(s):  
Medical Student ◽  
Medical Students ◽  
Medical School ◽  
Well Being ◽  
Daily Functioning ◽  
Wait List ◽  
School Students ◽  
Social Emotions ◽  
The Face ◽  
Second Year

Increasing data suggest that for medical school students the stress of academic and psychologicaldemands can impair social emotions that are a core aspect of compassion and ultimately physiciancompetence. Few interventions have proven successful for enhancing physician compassion inways that persist in the face of suffering and that enable sustained caretaker well-being. To addressthis issue, the current study was designed to (1) investigate the feasibility of cognitively-basedcompassion training (CBCT) for second-year medical students, and (2) test whether CBCT decreasesdepression, enhances compassion, and improves daily functioning in medical students. Comparedto the wait-list group, students randomized to CBCT reported increased compassion, and decreasedloneliness and depression. Changes in compassion were most robust in individuals reporting highlevels of depression at baseline, suggesting that CBCT may benefit those most in need by breakingthe link between personal suffering and a concomitant drop in compassion

Expectations about pain and analgesic treatment depend on medical providers’ facial appearances

2020 ◽  
Author(s):  
Elizabeth A. Necka ◽  
Carolyn Amir ◽  
Troy C. Dildine ◽  
Lauren Yvette Atlas
Keyword(s):  
Patient Outcomes ◽  
Visual Information ◽  
Healthcare Providers ◽  
Medical Procedures ◽  
Medical Providers ◽  
Analgesic Treatment ◽  
Digital Services ◽  
Receive Treatment ◽  
Facial Images ◽  
Trait Impressions

There is a robust link between patients’ expectations and clinical outcomes, as evidenced by the placebo effect. These expectations are shaped by the context surrounding treatment, including the patient-provider interaction. Prior work indicates that the provider’s behavior and characteristics, including warmth and competence, can shape patient outcomes. Yet humans rapidly form trait impressions of others prior to any in-person interaction. Here, we tested whether trait-impressions of hypothetical medical providers, based purely on facial images, influence participants’ choice of medical providers and expectations about their health following hypothetical medical procedures performed by those providers in a series of vignettes. Across five studies, participants selected providers who appeared more competent, based on facial visual information alone. Further, providers’ apparent competence predicted participants’ expectations about post-procedural pain and medication use. Participants’ perception of their similarity to providers also shaped expectations about pain and treatment outcomes. Our results suggest that humans develop expectations about their health outcomes prior to even setting foot in the clinic, based exclusively on first impressions. These findings have strong implications for health care, as individuals increasingly rely on digital services to choose healthcare providers, schedule appointments, and even receive treatment and care, a trend which is exacerbated as the world embraces telemedicine.

Educational Persistence in the Face of Violence

2018 ◽  
Vol 11 (2) ◽  
pp. 94-113 ◽  
Author(s):  
Adrian H. Huerta
Keyword(s):  
High School ◽  
High School Students ◽  
Urban Communities ◽  
Educational Aspirations ◽  
School Students ◽  
Male Youth ◽  
Educational Persistence ◽  
The Face ◽  
Latino Male ◽  
Forms Of Violence

Latino boys and young men often carry the debt of violence into different spaces. This invisible trauma manifests into disruptive behaviors in schools. It is well documented that violence in urban communities and schools has received significant attention from researchers, but little attention has been paid to Latino male youth as individuals and the various forms of violence they have experienced, and how that impacts educational persistence. This qualitative study focuses on 26 Latino male middle and high school students who are attending two continuation schools to understand the types of violence they have experienced and their educational aspirations after high school.

External sources promoting resilience in adults with intellectual disabilities: A systematic literature review

2020 ◽  
pp. 174462952096194 ◽  
Author(s):  
Femke Scheffers ◽  
Xavier Moonen ◽  
Eveline van Vugt
Keyword(s):  
Social Network ◽  
Literature Review ◽  
Intellectual Disabilities ◽  
Positive Emotions ◽  
Network Support ◽  
Increased Risk ◽  
The Social ◽  
The Face ◽  
External Sources ◽  
Adults With Intellectual Disabilities

Background: Persons with an intellectual disability are at increased risk of experiencing adversities. The current study aims at providing an overview of the research on how resilience in adults with intellectual disabilities, in the face of adversity, is supported by sources in their social network. Method: A literature review was conducted in the databases Psycinfo and Web of Science. To evaluate the quality of the included studies, the Mixed Method Appraisal Tool (MMAT) was used. Results: The themes: “ positive emotions,” “ network acceptance,” “ sense of coherence” and “ network support,” were identified as sources of resilience in the social network of the adults with intellectual disabilities. Conclusion: The current review showed that research addressing sources of resilience among persons with intellectual disabilities is scarce. In this first overview, four sources of resilience in the social network of people with intellectual disabilities were identified that interact and possibly strengthen each other.

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