Are People-Centered Intelligences Psychometrically Distinct from Thing-Centered Intelligences? A Meta-Analysis

The Social Shapes Test as a Self-Administered, Online Measure of Social Intelligence: Two Studies with Typically Developing Adults and Adults with Autism Spectrum Disorder

The Social Shapes Test (SST) is a measure of social intelligence which does not use human faces or rely on extensive verbal ability. The SST has shown promising validity among adults without autism spectrum disorder (ASD), but it is uncertain whether it is suitable for adults with ASD. We find measurement invariance between adults with (n = 229) or without ASD (n = 1,049) on the 23-item SST. We also find that adults without ASD score higher on the SST than adults with ASD (d = 0.21). We also provide two, 14-item versions which demonstrated good parallel test-retest reliability and are positively related to scores on the Frith-Happé task. The SST is suitable for remote, online research studies.

Seeing emotions in the eyes: a validated test to study individual differences in the perception of basic emotions

People are able to perceive emotions in the eyes of others and can therefore see emotions when individuals wear face masks. Research has been hampered by the lack of a good test to measure basic emotions in the eyes. In two studies respectively with 358 and 200 participants, we developed a test to see anger, disgust, fear, happiness, sadness and surprise in images of eyes. Each emotion is measured with 8 stimuli (4 male actors and 4 female actors), matched in terms of difficulty and item discrimination. Participants reliably differed in their performance on the Seeing Emotions in the Eyes test (SEE-48). The test correlated well not only with Reading the Mind in the Eyes Test (RMET) but also with the Situational Test of Emotion Understanding (STEU), indicating that the SEE-48 not only measures low-level perceptual skills but also broader skills of emotion perception and emotional intelligence. The test is freely available for research and clinical purposes.

Sentiment Analysis on Twitter: Role of Healthcare Professionals in the Global Conversation during the AstraZeneca Vaccine Suspension

The vaccines against COVID-19 arrived in Spain at the end of 2020 along with vaccination campaigns which were not free of controversy. The debate was fueled by the adverse effects following the administration of the AstraZeneca-Oxford (AZ) vaccine in some European countries, eventually leading to its temporary suspension as a precautionary measure. In the present study, we analyze the healthcare professionals' conversations, sentiment, polarity, and intensity on social media during two periods in 2021: the one closest to the suspension of the AZ vaccine and the same time frame 30 days later. We also analyzed whether there were differences between Spain and the rest of the world. Results: The negative sentiment ratio was higher (U = 87; p = 0.048) in Spain in March (Med = 0.396), as well as the daily intensity (U = 86; p = 0.044; Med = 0.440). The opposite happened with polarity (U = 86; p = 0.044), which was higher in the rest of the world (Med = -0.264). Conclusions: There was a general increase in messages and interactions between March and April. In Spain, there was a higher incidence of negative messages and intensity compared to the rest of the world during the March period that disappeared in April. Finally, it was found that the dissemination of messages linked to negative emotions towards vaccines against COVID-19 from healthcare professionals contributed to a negative approach to primary prevention campaigns in the middle of the pandemic.

EmBody/EmFace as a new open tool to assess emotion recognition from body and face expressions

Nonverbal expressions contribute substantially to social interaction by providing information on another person’s intentions and feelings. While emotion recognition from dynamic facial expressions has been widely studied, dynamic body expressions and the interplay of emotion recognition from facial and body expressions have attracted less attention, as suitable diagnostic tools are scarce. Here, we provide validation data on a new open source paradigm enabling the assessment of emotion recognition from both 3D-animated emotional body expressions (Task 1: EmBody) and emotionally corresponding dynamic faces (Task 2: EmFace). Both tasks use visually standardized items depicting three emotional states (angry, happy, neutral), and can be used alone or together. We here demonstrate successful psychometric matching of the EmBody/EmFace items in a sample of 217 healthy subjects with excellent retest reliability and validity (correlations with the Reading-the-Mind-in-the-Eyes-Test and Autism-Spectrum Quotient, no correlations with intelligence, and given factorial validity). Taken together, the EmBody/EmFace is a novel, effective (< 5 min per task), highly standardized and reliably precise tool to sensitively assess and compare emotion recognition from body and face stimuli. The EmBody/EmFace has a wide range of potential applications in affective, cognitive and social neuroscience, and in clinical research studying face- and body-specific emotion recognition in patient populations suffering from social interaction deficits such as autism, schizophrenia, or social anxiety.

Personal Intelligence Is Evident in the Sophistication of People's Narratives about Personality

Personal intelligence concerns the ability to understand personality in oneself and others-including the understanding of motives, socioemotional traits, and abilities. We examined if people's scores on the ability-based Test of Personal Intelligence (TOPI) would be reflected in their narratives about someone whose personality they had learned about. In a Preliminary Study (N = 220), we collected narratives and open-ended descriptions about their learning. In Study 1 (N = 212), experts rated the respondents' open-ended narratives for their sophistication about personality, defined as their knowledge and complexity of thought around the topic. Respondents also filled out checklists concerning what they learned and their relationship outcomes. Study 2 (N = 299) was a replication and extension in which we added the TOPI. Participants who scored higher on the TOPI produced narratives higher in Sophistication, even after statistical controls for Word Count and Vocabulary (the measures also were largely independent of the Big Five). The findings here may have applications for both testing and training.

Using game-like animations of geometric shapes to simulate social interactions: An evaluation of group score differences

This study introduces a novel, game-like method for measuring social intelligence: the Social Shapes Test. Unlike other existing video or game-based tests, the Shapes Test uses animations of abstract shapes to represent social interactions. We explore demographic differences in Shapes Test scores compared to a written situational judgment test. Gender and race/ethnicity only had meaningful effects on written SJT scores while no effects were found for Shapes Test scores. This pattern of results remained after controlling for general mental ability and English language exposure. We also found metric invariance between demographic groups for both tests. Our results demonstrate the potential for using animated shape tasks as an alternative to written SJTs when designing future game-based assessments.

Understanding the Emotional Intelligence Discourse on Social Media: Insights from the Analysis of Twitter

Social networks have created an information diffusion corpus that provides users with an environment where they can express their views, form a community, and discuss topics of similar or dissimilar interests. Even though there has been an increasingly rising demand for conducting an emotional analysis of the users on social media platforms, the field of emotional intelligence (EI) has been rather slow in exploiting the enormous potential that social media can play in the research and practice of the framework. This study, thus, tried to examine the role that the microblogging platform Twitter plays in enhancing the understanding of the EI community by building on the Twitter Analytics framework of Natural Language Processing to further develop the insights of EI research and practice. An analysis was conducted on 53,361 tweets extracted using the hashtag emotional intelligence through descriptive analytics (DA), content analytics (CA), and network analytics (NA). The findings indicated that emotional intelligence tweets are used mostly by speakers, psychologists (or other medical professionals), and business organizations, among others. They use it for information dissemination, communication with stakeholders, and hiring. These tweets carry strong positive sentiments and sparse connectedness. The findings present insights into the use of social media for understanding emotional intelligence.