Emotional intelligence and attentional bias for threat-related emotion under stress

AI for Psychometrics: Validating Machine Learning Models in Measuring Emotional Intelligence with Eye-Tracking Techniques

AI, or artificial intelligence, is a technology of creating algorithms and computer systems that mimic human cognitive abilities to perform tasks. Many industries are undergoing revolutions due to the advances and applications of AI technology. The current study explored a burgeoning field-Psychometric AI, which integrates AI methodologies and psychological measurement to not only improve measurement accuracy, efficiency, and effectiveness but also help reduce human bias and increase objectivity in measurement. Specifically, by leveraging unobtrusive eye-tracking sensing techniques and performing 1470 runs with seven different machine-learning classifiers, the current study systematically examined the efficacy of various (ML) models in measuring different facets and measures of the emotional intelligence (EI) construct. Our results revealed an average accuracy ranging from 50-90%, largely depending on the percentile to dichotomize the EI scores. More importantly, our study found that AI algorithms were powerful enough to achieve high accuracy with as little as 5 or 2 s of eye-tracking data. The research also explored the effects of EI facets/measures on ML measurement accuracy and identified many eye-tracking features most predictive of EI scores. Both theoretical and practical implications are discussed.

Positive attentional bias mediates the relationship between trait emotional intelligence and trait affect

Emotional intelligence and, in particular, the component emotion regulation may increase well-being and improve mood and coping with negative emotions. In the present eye-tracking study, we examined whether attention allocation to positive stimuli mediates the relationship between emotion regulation abilities and trait affect. Gaze behavior of 104 healthy adults was analyzed in a free-viewing task, in which happy, sad, angry, and neutral faces were shown simultaneously for ten seconds. Dwell time on facial expressions was used as indicator of attention allocation. Trait emotional intelligence was assessed using the Self-Rated Emotional Intelligence Scale. Self-report measures of state and trait positive and negative affect, trait anxiety, and depression were administered. In general, participants viewed longer at happy than at negative or neutral faces. The results of mediation analyses indicated that intrapersonal and interpersonal emotion regulation abilities were indirectly related to trait positive affect through attention to happy faces. Moreover, dwell time on happy faces had a mediating effect on the relationship between interpersonal emotion regulation ability and trait anxiety. Preference for positive social signals might form one attentional pathway through which emotion regulation abilities promote positive mood and buffer the development of anxiety reactions in everyday life.

Insular Dichotomy in the Implicit Detection of Emotions in Human Faces

The functional roles of the insula diverge between its posterior portion (PI), mainly connected with somato-sensory and motor areas, and its anterior section (AI) connected with the frontal, limbic, and cingulate regions. We report intracranial recordings of local field evoked potentials from PI, AI, and the visual fusiform gyrus to a full array of emotional faces including pain while the individuals' attention was diverted from emotions. The fusiform gyrus and PI responded equally to all types of faces, including neutrals. Conversely, the AI responded only to emotional faces, maximally to pain and fear, while remaining insensitive to neutrals. The two insular sectors reacted with almost identical latency suggesting their parallel initial activation via distinct functional routes. The consistent responses to all emotions, together with the absence of response to neutral faces, suggest that early responses in the AI reflect the immediate arousal value and behavioral relevance of emotional stimuli, which may be subserved by "fast track" routes conveying coarse-spatial-frequency information via the superior colliculus and dorsal pulvinar. Such responses precede the conscious detection of the stimulus' precise signification and valence, which need network interaction and information exchange with other brain areas, for which the AI is an essentialhub.

The relationship between dispositional attention to feelings and visual attention to emotion

Attention to feelings is a core dimension of individual differences in the perception of one's emotions. It concerns the frequency with which own emotions are attended to. The aim of the present eye-tracking investigation was to examine the relationship of dispositional attention to feelings with early and late attentional processes in the visual perception of emotional information. Attentional orientation was assessed in a sample of healthy women (N = 91) using eye-tracking during a free viewing task in which images with positive, negative and neutral content were shown simultaneously. Pictures were taken from the Besançon Affective Picture Set. State and trait affect, depression, and intelligence of participants were controlled. In our sample, attention to feelings was not related to positive affect, negative affect, depression or intelligence. Attention to feelings was negatively correlated with entry times for all emotional picture types. Moreover, attention to feelings was positively correlated with dwell time on positive images and negatively correlated with dwell time on neutral images. Our data indicate that devoting habitually attention to one's feelings is linked to an enhanced initial orientation of attention towards emotional scenes, irrespective of affective valence. Dispositional attention to feelings might also be associated with an attentional preference for emotional over neutral visual stimuli. The present findings on general individual differences in attention to feelings could have important implications for future clinical research on attentional biases.

Does Emotional Intelligence Buffer the Effects of Acute Stress? A Systematic Review

People with higher levels of emotional intelligence (EI: adaptive emotional traits, skills, and abilities) typically achieve more positive life outcomes, such as psychological wellbeing, educational attainment, and job-related success. Although the underpinning mechanisms linking EI with those outcomes are largely unknown, it has been suggested that EI may work as a "stress buffer." Theoretically, when faced with a stressful situation, emotionally intelligent individuals should show a more adaptive response than those with low EI, such as reduced reactivity (less mood deterioration, less physiological arousal), and faster recovery once the threat has passed. A growing number of studies have begun to investigate that hypothesis in respect to EI measured as both an ability (AEI) and trait (TEI), but results are unclear. To test the "stress-buffering" function of EI, we systematically reviewed experimental studies that explored the relationship between both types of EI and acute stress reactivity or recovery. By searching four databases, we identified 45 eligible studies. Results indicated that EI was only adaptive in certain contexts, and that findings differed according to stressor type, and how EI was measured. In terms of stress reactivity, TEI related to less mood deterioration during sports-based stressors (e.g., competitions), physical discomfort (e.g., dental procedure), and cognitive stressors (e.g., memory tasks), but did not appear as helpful in other contexts (e.g., public speaking). Furthermore, effects of TEI on physiological stress responses, such as heart rate, were inconsistent. Effects of AEI on subjective and objective stress reactivity were often non-significant, with high levels detrimental in some cases. However, data suggest that both higher AEI and TEI relate to faster recovery from acute stress. In conclusion, results provide mixed support for the stress-buffering effect of EI. Limitations and quality of studies are also discussed. Findings could have implications for EI training programmes.

Putting 'Emotional Intelligences' in Their Place: Introducing the Integrated Model of Affect-Related Individual Differences

Numerous individual differences, models, and measures have been associated with the ‘emotional intelligence’ (EI) label. This paper discusses one of the most pervasive problems regarding EI-related individual differences, namely, the lack of a meaningful theoretical framework. First, drawing upon existing theoretical frameworks, we argue that EI-related characteristics can be considered constituents of existing models of cognitive ability (ability EI), personality (trait EI), and emotion regulation (EI competencies). Second, having differentiated between these perspectives (ability, personality, and emotion regulation), we draw upon existing theory and research to build the Integrated Model of Affect-related Individual Differences (IMAID), which provides an initial mechanistic representation that explains how the different EI-related constructs are likely to interrelate and coalesce to influence affective outcomes. In essence, the IMAID is an integrated mediation model in which emotion regulation mediates the effects of ability EI and affect-related personality traits upon outcomes. Viewing EI-related constructs as interrelated extensions of well-established individual difference frameworks clarifies some pervasive misconceptions regarding EI-related characteristics and provides scholars and practitioners with a clear and useful theoretical framework ripe for exploration. We conclude by using the IMAID to suggest a theoretically driven agenda for future research.