Four dimensions characterize comprehensive trait judgments of faces

People readily attribute many traits to faces: some look beautiful, some competent, some aggressive. These snap judgments have important consequences in real life, ranging from success in political elections to decisions in courtroom sentencing. 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 in social and developmental psychology, social neuroscience, and in 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, and reconcile prior work on face perception with work in social cognition and personality psychology

Authenticating newly constructed ancient towns in China: A critical discourse analysis

Newly constructed ancient towns (NCAT) in China are tourist attractions that are designed to reproduce and revitalize traditional Chinese culture. Past researches regarded NCATs as either commercial spaces or imitated heritage sites. The dynamic relationship between NCATs and the commonly accepted truths about history and culture is underexplored. This paper regards NCATs as a social force to reproduce history and culture, and investigates how invented traditions can be naturalized within NCATs. Authentication theory was applied to a case study of Gubei water town. Data was collected with methods of ethnography and netnography. Critical discourse analysis was adopted to analyze data. Results show that old materials and transplanted plants are proofs that were reassembled to naturalize and historicize invented traditions. This process of inventing traditions is concealed and converted by the discourse, believing that historical inheritance is inherently determined by natural powers. Purposefully-invented traditions are thus authenticated as naturally-determined traditions. Tourists accept and spread the official interpretation as objective knowledge, despite that they know Gubei water town is newly constructed. Tourists value old materials because they provide a physical base affording individual imaginations of history. Tourism practices do not reflect but create the ‘truth’. Tourism developers should contemplate the values conveyed within tourist sites

The study of the relationship between brand image and CSR purchasing behavior

JEL Classification: M1, M14Corporate social responsibility (CSR) has become a hot topic in the recent years. More and more companies start to adopt the CSR strategy and implement the CSR behavior in order to satisfy their stakeholders in the world. Consumer, as one of the critical member of stakeholders, can positively or negatively affect or be affected by the CSR performance of the company. On the other hand, in the marketing aspect, brand image plays an important role in differentiation and value creation. In addition, brand image can affect the purchasing behavior of consumer through a series of the complex psychological process. In this dissertation, we are going to study the CSR perception of Chinese consumers, and the relationship among CSR perception, brand image and CSR purchasing behavior. We start the quantitative research by developing a questionnaire, which is handed out to the Chinese respondents through WeChat. In the end, there are two interesting findings in the research: (1) brand image plays as a complete mediator in mediating the relationship between brand awareness and CSR purchasing behavior; (2) CSR perception plays as a complete mediator in mediating the relationship between brand image and CSR purchasing behavior.A Responsabilidade Social das Empresas (RSE) tem sido muito debatida nos últimos anos. Mais e mais empresas começaram a implementar a Responsabilidade Social para satisfazer os seus stakeholders. Os consumidores, enquanto um dos stakehoders mais críticos, podem ser positivamente ou negativamente afetados pelo desempenho da RSE. Por um lado, no aspeto de marketing, a imagem da marca tem um papel importante na diferenciação e criação de valor. Para além disso, a imagem da marca pode afetar o comportamento de compra através de um processo psicológico complexo. Nesta dissertação, iremos estudar a perceção referente à RSE dos consumidores Chineses e a relação entre a perceção da RSE, a imagem de marca e o comportamento de compra. Iniciamos a análise quantitativa pelo desenvolvimento de um questionário, que foi distribuído pelo WeChat. Da pesquisa efetuada destacamos dois resultados: (1) a imagem de marca desempenha um papel de mediador ao mediar a relação entre a consciência de marca e comportamento de compra socialmente responsável; (2) a perceção de responsabilidade social desempenha um papel de mediador ao mediar a relação entre imagem de marca e comportamento de compra responsável

Personality traits are directly associated with anti-black prejudice in the United States

Modern psychological theories postulate that individual differences in prejudice are determined by social and ideological attitudes instead of personality. For example, the dual-process motivational (DPM) model argues that personality does not directly associate with prejudice when controlling for the attitudinal variables that capture the authoritarian-conservatism motivation and the dominance motivation. Previous studies testing the DPM model largely relied on convenience samples and/or European samples, and have produced inconsistent results. Here we examined the extent to which anti-black prejudice was associated with the Big Five personality traits and social and ideological attitudes (authoritarianism, social dominance orientation, political party affiliation) in two large probability samples of the general population (N₁ = 3,132; N₂ = 2,483) from the American National Election Studies (ANES). We performed structural equation modeling (SEM) to test the causal assumptions between the latent variables and used survey weights to generate estimates that were representative of the population. Different from prior theories, across both datasets we found that two personality traits, agreeableness and conscientiousness, were directly associated with anti-black prejudice when controlling for authoritarianism, social dominance orientation, and political party affiliation. We also found that a substantial part of the associations between personality traits and anti-black prejudice were mediated through those social and ideological attitudes, which might serve as candidates for prejudice-reduction interventions in the real world

Inferring Whether Officials Are Corruptible From Looking At Their Faces

While inferences of traits from unfamiliar faces prominently reveal stereotypes, some facial inferences also correlate with real-world outcomes. We investigated whether facial inferences are associated with an important real-world outcome closely linked to the face bearer’s behavior: political corruption. In four preregistered studies (N = 325), participants made trait judgments of unfamiliar government officials on the basis of their photos. Relative to peers with clean records, federal and state officials convicted of political corruption (Study 1) and local officials who violated campaign finance laws (Study 2) were perceived as more corruptible, dishonest, selfish, and aggressive but similarly competent, ambitious, and masculine (Study 3). Mediation analyses and experiments in which the photos were digitally manipulated showed that participants’ judgments of how corruptible an official looked were causally influenced by the face width of the stimuli (Study 4). The findings shed new light on the complex causal mechanisms linking facial appearances with social behavior

Efficient prediction of trait judgments from faces using deep neural networks

Judgments of people from their faces are often invalid but influence many social decisions (e.g., legal sentencing), making them an important target for automated prediction. Direct training of deep convolutional neural networks (DCNNs) is difficult because of sparse human ratings, but features obtained from DCNNs pre-trained on other classifications (e.g., object recognition) can predict trait judgments within a given face database. However, it remains unknown if this latter approach generalizes across faces, raters, or traits. Here we directly compare three distinct types of face features, and test them across multiple out-of-sample datasets and traits. DCNNs pre-trained on face identification provided features that generalized the best, and models trained to predict a given trait also predicted several other traits. We demonstrate the flexibility, generalizability, and efficiency of using DCNN features to predict human trait judgments from faces, providing an easily scalable framework for automated prediction of human judgment

A Maximum Likelihood Method with Penalty to Estimate Link Travel Time Based on Trip Itinerary Data

Travel time is an important network performance measure. It is a challenging subject due to the fluctuations in traffic characteristics, such as traffic flow. This study proposes a maximum likelihood method with penalty to estimate link travel time based on trip itinerary data from a statistical point. Three penalized models, which are Lasso penalized model, Ridge penalized model and Revised-Lasso penalized model, are introduced. The models are discussed and compared with the basic model which is a maximum likelihood function without penalty. First, the predictive performance of the basic model and three penalized models are evaluated based on the data of three simulated networks. Results suggest that Revised-Lasso penalized model outperforms other models. In this research, Revised-Lasso penalized model is applied to a simplified Sioux Falls network. This study also provides a detailed procedure to estimate link travel time parameters in the simplified Sioux Falls network. Finally, the effect of the sample size on estimation accuracy is tested. The results show that sample size has a significant effect on the basic model estimation, but it has little effect on the Revised-Lasso penalized model estimation. This study provides an efficient and accurate way to estimate link travel time distribution