The present study examined how neuroticism, extraversion, and emotion regulation were related to loneliness and well-being during 6 weeks of major public life restrictions in the Covid-19 pandemic in Switzerland. Cross-sectional results from 466 participants showed that neuroticism and emotion regulation strategies were associated with higher loneliness and lower well-being. However, in contrast to prior research, associations of extraversion with loneliness and well-being were weak and were qualified by interactions with emotion regulation. For introverts, maladaptive cognitive strategies such as rumination or catastrophizing were related to higher levels of loneliness. For extraverts, emotion suppression was related to lower levels of affective well-being. Individuals with low maladaptive regulation reported higher well-being the longer the public life restrictions were in place at the time of study participation. These findings suggest that first, extraversion may lose some of its protective value for loneliness and well-being when opportunities to engage in social activities are limited; second, that loneliness and well-being do not decrease over 6 weeks of public life restrictions; and third, that future studies should further investigate the moderating role of emotion regulation on the link between personality, loneliness, and well-being.

The ability to recognise others’ emotions from nonverbal cues (emotion recognition ability, ERA) is measured with performance-based tests and has many positive correlates. Although researchers have long proposed that ERA is related to general mental ability or intelligence, a comprehensive analysis of this relationship is lacking. For instance, it remains unknown whether the magnitude of the association varies by intelligence type, ERA test features, as well as demographic variables. The present meta-analysis examined the relationship between ERA and intelligence based on 471 effect sizes from 133 samples and found a significant mean effect size (controlled for nesting within samples) of r = .19. Different intelligence types (crystallized, fluid, spatial, memory, information processing speed and efficiency) yielded similar effect sizes, whereas academic achievement measures (e.g. SAT scores) were unrelated to ERA. Effect sizes were higher for ERA tests that simultaneously present facial, vocal, and bodily cues (as compared to tests using static pictures) and for tests with higher reliability and more emotions. Results were unaffected by most study and sample characteristics, but effect size increased with higher mean age of the sample. These findings establish ERA as sensory-cognitive ability that is distinct from, yet related to, intelligence.

Large Language Models (LLMs) demonstrate expertise across diverse domains, yet their capacity for emotional intelligence remains uncertain. This research examined whether LLMs can solve and generate performance-based emotional intelligence tests. Results showed that ChatGPT-4, ChatGPT-o1, Gemini 1.5 flash, Copilot 365, Claude 3.5 Haiku, and DeepSeek V3 outperformed humans on five standard emotional intelligence tests, achieving an average accuracy of 81%, compared to the 56% human average reported in the original validation studies. In a second step, ChatGPT-4 generated new test items for each emotional intelligence test. These new versions and the original tests were administered to human participants across five studies (total N = 467). Overall, original and ChatGPT-generated tests demonstrated statistically equivalent test difficulty. Perceived item clarity and realism, item content diversity, internal consistency, correlations with a vocabulary test, and correlations with an external ability emotional intelligence test were not statistically equivalent between original and ChatGPT-generated tests. However, all differences were smaller than Cohen’s d ± 0.25, and none of the 95% confidence interval boundaries exceeded a medium effect size (d ± 0.50). Additionally, original and ChatGPT-generated tests were strongly correlated (r = 0.46). These findings suggest that LLMs can generate responses that are consistent with accurate knowledge about human emotions and their regulation.

To investigate how medical students’ empathy is related to their mental health and burnout.This cross-sectional study included 886 medical students from curriculum years 1–6. The cognitive, affecti...

Emotion understanding, which can broadly be defined as expertise in the meaning of emotion, is a core component of emotional intelligence and facilitates better intra- and interpersonal outcomes. However, to date only very few standard tests to measure emotion understanding in healthy adults exist. Here, we present two new performance-based tests that were developed and are scored based on componential emotion theory and large-scale cross-cultural empirical findings. These instruments intend to measure facets of emotion understanding that are not included in existing tests. The first test (Geneva EMOtion Knowledge test – Blends; GEMOK-Blends) measures the ability to understand and label emotional experiences of a target person from a description of emotion features covering five emotion components (appraisal, feeling, action tendencies, expression, and physiology) embedded in a written vignette. The second test (GEMOK-Features) measures semantic knowledge about which features from each component are characteristic of emotion episodes described by a specific emotion label. In four studies, we found evidence for the good internal consistency and construct validity of these tests. Both tests were positively correlated with other emotional abilities and cognitive ability and showed meaningful associations with a variety of personality and demographic variables.