scholarly journals Four dimensions characterize attributions from faces using a representative set of English trait words

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chujun Lin ◽  
Umit Keles ◽  
Ralph Adolphs
Keyword(s):  
Large Scale ◽  
Three Dimensions ◽  
Four Dimensions ◽  
Individual Level ◽  
Social Traits ◽  
Level Data ◽  
Multiple Regions ◽  
Face Stimuli ◽  
Trait Words ◽  
Psychological Dimensions

AbstractPeople readily (but often inaccurately) attribute traits to others based on faces. While the details of attributions depend on the language available to describe social traits, psychological theories argue that two or three dimensions (such as valence and dominance) summarize social trait attributions from faces. However, prior work has used only a small number of trait words (12 to 18), limiting conclusions to date. In two large-scale, preregistered studies we ask participants to rate 100 faces (obtained from existing face stimuli sets), using a list of 100 English trait words that we derived using deep neural network analysis of words that have been used by other participants in prior studies to describe faces. In study 1 we find that these attributions are best described by four psychological dimensions, which we interpret as “warmth”, “competence”, “femininity”, and “youth”. In study 2 we partially reproduce these four dimensions using the same stimuli among additional participant raters from multiple regions around the world, in both aggregated and individual-level data. These results provide a comprehensive characterization of trait attributions from faces, although we note our conclusions are limited by the scope of our study (in particular we note only white faces and English trait words were included).

scholarly journals Four dimensions characterize comprehensive trait judgments of faces

2019 ◽  
Author(s):  
Chujun Lin ◽  
Umit Keles ◽  
Ralph Adolphs
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Three Dimensions ◽  
Prior Work ◽  
Four Dimensions ◽  
Individual Level ◽  
Level Data ◽  
Trait Words ◽  
Psychological Dimensions

People readily attribute many traits to faces: some look beautiful, some competent, some aggressive1. These snap judgments have important consequences in real life, ranging from success in political elections to decisions in courtroom sentencing2,3. Modern psychological theories argue that the hundreds of different words people use to describe others from their faces are well captured by only two or three dimensions, such as valence and dominance4, a highly influential framework that has been the basis for numerous studies in social and developmental psychology5–10, social neuroscience11,12, and in engineering applications13,14. However, all prior work has used only a small number of words (12 to 18) to derive underlying dimensions, limiting conclusions to date. Here we employed deep neural networks to select a comprehensive set of 100 words that are representative of the trait words people use to describe faces, and to select a set of 100 faces. In two large-scale, preregistered studies we asked participants to rate the 100 faces on the 100 words (obtaining 2,850,000 ratings from 1,710 participants), and discovered a novel set of four psychological dimensions that best explain trait judgments of faces: warmth, competence, femininity, and youth. We reproduced these four dimensions across different regions around the world, in both aggregated and individual-level data. These results provide a new and most comprehensive characterization of face judgments, and reconcile prior work on face perception with work in social cognition15 and personality psychology16.

scholarly journals Four dimensions characterizing comprehensive trait judgments of faces

2020 ◽  
Author(s):  
Chujun Lin ◽  
Umit Keles ◽  
Ralph Adolphs
Keyword(s):  
Large Scale ◽  
Developmental Psychology ◽  
Three Dimensions ◽  
Four Dimensions ◽  
Individual Level ◽  
Psychological Theories ◽  
Level Data ◽  
Trait Words ◽  
Psychological Dimensions

Abstract People readily attribute many traits to faces: some look beautiful, some competent, some aggressive. Modern psychological theories argue that the hundreds of different words people use to describe others from their faces are well captured by only two or three dimensions, such as valence and dominance, a highly influential framework that has been the basis for numerous studies across social and developmental psychology, social neuroscience, and engineering applications. However, all prior work has used only a small number of words (12 to 18) to derive underlying dimensions, limiting conclusions to date. Here we employed deep neural networks to select a comprehensive set of 100 words that are representative of the trait words people use to describe faces, and to select a set of 100 faces. In two large-scale, preregistered studies we asked participants to rate the 100 faces on the 100 words (obtaining 2,850,000 ratings from 1,710 participants), and discovered a novel set of four psychological dimensions that best explain trait judgments of faces: warmth, competence, femininity, and youth. We reproduced these four dimensions across different regions around the world, in both aggregated and individual-level data. These results provide a new and most comprehensive characterization of face judgments.

scholarly journals Apples and Oranges? The Problem of Equivalence in Comparative Research

2011 ◽  
Vol 19 (4) ◽  
pp. 471-487 ◽  
Author(s):  
Daniel Stegmueller
Keyword(s):  
Item Response ◽  
Comparative Research ◽  
Large Scale ◽  
Structural Parameters ◽  
Simultaneous Estimation ◽  
Response Behavior ◽  
Item Response Model ◽  
Individual Level ◽  
Level Data ◽  
Cross National

Researchers in comparative research are increasingly relying on individual level data to test theories involving unobservable constructs like attitudes and preferences. Estimation is carried out using large-scale cross-national survey data providing responses from individuals living in widely varying contexts. This strategy rests on the assumption of equivalence, that is, no systematic distortion in response behavior of individuals from different countries exists. However, this assumption is frequently violated with rather grave consequences for comparability and interpretation. I present a multilevel mixture ordinal item response model with item bias effects that is able to establish equivalence. It corrects for systematic measurement error induced by unobserved country heterogeneity, and it allows for the simultaneous estimation of structural parameters of interest.

scholarly journals Impact of increasing vegetarian availability on meal selection and sales in cafeterias

2019 ◽  
Vol 116 (42) ◽  
pp. 20923-20929 ◽  
Author(s):  
Emma E. Garnett ◽  
Andrew Balmford ◽  
Chris Sandbrook ◽  
Mark A. Pilling ◽  
Theresa M. Marteau
Keyword(s):  
Large Scale ◽  
Population Level ◽  
Field Studies ◽  
Individual Level ◽  
Rebound Effects ◽  
Percentage Points ◽  
Physical Environments ◽  
Meat Meal ◽  
Level Data ◽  
The Impact

Shifting people in higher income countries toward more plant-based diets would protect the natural environment and improve population health. Research in other domains suggests altering the physical environments in which people make decisions (“nudging”) holds promise for achieving socially desirable behavior change. Here, we examine the impact of attempting to nudge meal selection by increasing the proportion of vegetarian meals offered in a year-long large-scale series of observational and experimental field studies. Anonymized individual-level data from 94,644 meals purchased in 2017 were collected from 3 cafeterias at an English university. Doubling the proportion of vegetarian meals available from 25 to 50% (e.g., from 1 in 4 to 2 in 4 options) increased vegetarian meal sales (and decreased meat meal sales) by 14.9 and 14.5 percentage points in the observational study (2 cafeterias) and by 7.8 percentage points in the experimental study (1 cafeteria), equivalent to proportional increases in vegetarian meal sales of 61.8%, 78.8%, and 40.8%, respectively. Linking sales data to participants’ previous meal purchases revealed that the largest effects were found in the quartile of diners with the lowest prior levels of vegetarian meal selection. Moreover, serving more vegetarian options had little impact on overall sales and did not lead to detectable rebound effects: Vegetarian sales were not lower at other mealtimes. These results provide robust evidence to support the potential for simple changes to catering practices to make an important contribution to achieving more sustainable diets at the population level.

scholarly journals Approximate conditional phenotype analysis based on genome wide association summary statistics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peitao Wu ◽  
Biqi Wang ◽  
Steven A. Lubitz ◽  
Emelia J. Benjamin ◽  
James B. Meigs ◽  
...  
Keyword(s):  
Large Scale ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Summary Statistics ◽  
Phenotypic Data ◽  
Individual Level ◽  
Genome Wide ◽  
Level Data ◽  
A Genome ◽  
Phenotype Analysis

AbstractBecause single genetic variants may have pleiotropic effects, one trait can be a confounder in a genome-wide association study (GWAS) that aims to identify loci associated with another trait. A typical approach to address this issue is to perform an additional analysis adjusting for the confounder. However, obtaining conditional results can be time-consuming. We propose an approximate conditional phenotype analysis based on GWAS summary statistics, the covariance between outcome and confounder, and the variant minor allele frequency (MAF). GWAS summary statistics and MAF are taken from GWAS meta-analysis results while the traits covariance may be estimated by two strategies: (i) estimates from a subset of the phenotypic data; or (ii) estimates from published studies. We compare our two strategies with estimates using individual level data from the full GWAS sample (gold standard). A simulation study for both binary and continuous traits demonstrates that our approximate approach is accurate. We apply our method to the Framingham Heart Study (FHS) GWAS and to large-scale cardiometabolic GWAS results. We observed a high consistency of genetic effect size estimates between our method and individual level data analysis. Our approach leads to an efficient way to perform approximate conditional analysis using large-scale GWAS summary statistics.

Corruption and its Impact on Protest Activities in Post-communist Europe

2021 ◽  
pp. 1-15
Author(s):  
Milan Školník
Keyword(s):  
Political Participation ◽  
Large Scale ◽  
Role Of Government ◽  
Individual Level ◽  
Level Data ◽  
Public Protest ◽  
State Administration ◽  
Communist Countries ◽  
The Impact

Corruption is a phenomenon that affects societies. It lowers trust in public institutions, lowers trust among people, undermines economic development, undermines democracy, and has implications for political participation. This article contributes to current debates on the impact of corruption by looking at other possible consequences of corruption. Specifically, this article looks at the impact of the perception of corruption on the approval of public protest meetings and demonstrations because, if corruption leads to these non-institutionalized forms of political participation, this may lead to security problems or a direct outbreak of violence. This study analyses this relationship by using seven post-communist countries that have undergone specific developments in terms of corruption. These developments were largely due to large-scale privatizations, politicized state administration, and the linking of politicians to the private sector. This research was conducted with individual-level data. The module ‘The Role of Government V’ from the International Social Survey Programme was used. Descriptive charts have revealed that in six out of the seven countries, most respondents considered politicians to be very corrupt. Around 80% of respondents in all seven countries approve of the organization of public protest meetings. Around 70% of respondents in all seven countries approve of demonstrations. Regression analysis revealed that there is a relationship between the perception of corruption among politicians and the approval of protest activities. Specifically, the more politicians are corrupt, the more people approve of holding public protest meetings and demonstrations.

scholarly journals Bibliometric factors associated with h-index of Peruvian researchers with publications indexed on Web of Science and Scopus databases

2014 ◽  
Vol 26 (2) ◽  
pp. 143-154 ◽  
Author(s):  
Carlos Vílchez-Román
Keyword(s):  
External Validity ◽  
Large Scale ◽  
Web Of Science ◽  
Linear Regression Analysis ◽  
H Index ◽  
Institutional Level ◽  
Individual Level ◽  
Factors Associated ◽  
Level Data ◽  
Stepwise Procedure

The objective of this article is: a) to identify Peruvian researchers with high, medium and low impact factor according to Web of Science and Scopus databases; b) to identify the bibliometric factor with the highest influence on h-index of Peruvian esearchers; c) to compare h-index between Web of Science and Scopus, at an individual and institutional level. Data were collected from Web of Science and Scopus (189 Peruvian researchers, 28 institutions on Web of Science and 33 on Scopus), between September 1823, 2013. Then, institutional registries were created and linear regression analysis with stepwise procedure was run to identify bibliometric factors with higher influence on the h-index of Peruvian researchers. Web of Science and Scopus showed interesting simmilarities in the h-index of Peruvian academic institutions. At individual level, documents indexed in citation database had the highest influence on the h-index. Regression model identified bibliometric factors with higher influence on the h-index of Peruvian researchers, however further large scale studies are needed to improve external validity.

Evaluation of Large-scale Data to Detect Irregularity in Payment for Medical Services

2016 ◽  
Vol 55 (03) ◽  
pp. 284-291
Author(s):  
Junghyun Park ◽  
Seokjoon Yoon ◽  
Minki Kim
Keyword(s):  
Large Scale ◽  
Healthcare Sector ◽  
Benford’S Law ◽  
Individual Level ◽  
Large Scale Data ◽  
Level Data ◽  
Depth Analysis ◽  
Benford's Law ◽  
The Individual ◽  
Scale Data

SummaryBackground: Sophisticated anti-fraud systems for the healthcare sector have been built based on several statistical methods. Although existing methods have been developed to detect fraud in the healthcare sector, these algorithms consume considerable time and cost, and lack a theoretical basis to handle large-scale data.Objectives: Based on mathematical theory, this study proposes a new approach to using Benford’s Law in that we closely examined the individual-level data to identify specific fees for in-depth analysis.Methods: We extended the mathematical theory to demonstrate the manner in which large-scale data conform to Benford’s Law. Then, we empirically tested its applicability using actual large-scale healthcare data from Korea’s Health Insurance Review and Assessment (HIRA) National Patient Sample (NPS). For Benford’s Law, we considered the mean absolute deviation (MAD) formula to test the large-scale data.Results: We conducted our study on 32 diseases, comprising 25 representative diseases and 7 DRG-regulated diseases. We performed an empirical test on 25 diseases, showing the applicability of Benford’s Law to large-scale data in the healthcare industry. For the seven DRG-regulated diseases, we examined the individual-level data to identify specific fees to carry out an in-depth analysis. Among the eight categories of medical costs, we considered the strength of certain irregularities based on the details of each DRG-regulated disease.Conclusions: Using the degree of abnormality, we propose priority action to be taken by government health departments and private insurance institutions to bring unnecessary medical expenses under control. However, when we detect deviations from Benford’s Law, relatively high contamination ratios are required at conventional significance levels.

scholarly journals Bayesian large-scale multiple regression with summary statistics from genome-wide association studies

2016 ◽  
Author(s):  
Xiang Zhu ◽  
Matthew Stephens
Keyword(s):  
Multiple Regression ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Individual Level ◽  
Genome Wide ◽  
Level Data ◽  
Wide Range

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously-proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously-unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss.

scholarly journals Deep integrative models for large-scale human genomics

2021 ◽  
Author(s):  
Arnor Ingi Sigurdsson ◽  
David Westergaard ◽  
Ole Winther ◽  
Ole Lund ◽  
Søren Brunak ◽  
...  
Keyword(s):  
Large Scale ◽  
Linear Models ◽  
Critical Role ◽  
Measurement Data ◽  
Risk Scores ◽  
Individual Level ◽  
Automatic Integration ◽  
Level Data ◽  
Wide Range ◽  
Clinical Measurements

Polygenic risk scores (PRSs) are expected to play a critical role in achieving precision medicine. PRS predictors are generally based on linear models using summary statistics, and more recently individual- level data. However, these predictors generally only capture additive relationships and are limited when it comes to what type of data they use. Here, we develop a deep learning framework (EIR) for PRS prediction which includes a model, genome-local-net (GLN), we specifically designed for large scale genomics data. The framework supports multi-task (MT) learning, automatic integration of clinical and biochemical data and model explainability. GLN outperforms LASSO for a wide range of diseases, particularly autoimmune disease which have been researched for interaction effects. We showcase the flexibility of the framework by training one MT model to predict 338 diseases simultaneously. Furthermore, we find that incorporating measurement data for PRSs improves performance for virtually all (93%) diseases considered (ROC-AUC improvement up to 0.36) and that including genotype data provides better model calibration compared to measurements alone. We use the framework to analyse what our models learn and find that they learn both relevant disease variants and clinical measurements. EIR is open source and available at https://github.com/arnor-sigurdsson/EIR.

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