Effects of Self-Esteem on Self-Viewing: An Eye-Tracking Investigation on Mirror Gazing

Evaluations of dyadic synchrony: observers' traits influence estimation and enjoyment of synchrony in mirror-game movements

While evidence abounds that motor synchrony is a powerful form of 'social glue' for those involved, we have yet to understand how observers perceive motor synchrony: can observers estimate the degree of synchrony accurately? Is synchrony aesthetically pleasing? In two preregistered experiments (n = 161 each), we assess how accurately observers can estimate the degree of synchrony in dyads playing the mirror game, and how much observers enjoy watching these movements. We further assess whether accuracy and enjoyment are influenced by individual differences in self-reported embodied expertise (ability to reproduce movements, body awareness, body competence), psychosocial resources (extraversion, self-esteem), or social competencies (empathy, autistic traits), while objectively controlling for the degree of measured synchrony and complexity. The data revealed that observers' estimated synchrony with poor accuracy, showing a tendency to underestimate the level of synchrony. Accuracy for low synchrony improved with increasing body competence, while accuracy for high synchrony improved with increasing autistic traits. Observers' enjoyment of dyadic movements correlated positively with the degree of measured synchrony, the predictability of the movements, and the observer's empathy. Furthermore, very low enjoyment was associated with increased body perception. Our findings indicate that accuracy in perceiving synchrony is closely linked to embodiment, while aesthetic evaluations of action hinge on individual differences.

(No) Effects of a Self-Kindness Intervention on Self-Esteem and Visual Self-Perception: An Eye-Tracking Investigation on the Time-Course of Self-Face Viewing

Previous research has suggested a favorable impact of self-kindness on subjective well-being. The present experiment investigated the effects of an app-assisted self-kindness intervention for increasing self-esteem and self-face gaze, and for decreasing depression. We explored self-face processing via a time-course analysis of eye-tracking data. Eighty participants (56 female, 24 male; mean age: 23.2 years) were randomly allocated to one of two intervention groups, each receiving daily instructions to enhance either self-kindness or relaxation (active control). Following a one-week intervention period, both groups reported improved self-esteem (p = .035, ηpart2 = .068) and reduced depression (p < .001, ηpart2 = .17). The duration of self-face gaze increased in both groups (p < .001, ηpart2 = .21). Self-face processing was characterized by an early automatic attention bias toward the self-face, with a subsequent reduction in self-face bias, followed in turn by an attentional self-face reapproach, and then a stable self-face bias. We thus identified a complex temporal pattern of self-face inspection, which was not specifically altered by the intervention. This research sheds light on the potential for app-assisted interventions to positively impact psychological well-being, while also highlighting the complexity of self-face processing dynamics in this context. In the future, we propose the inclusion of personalized self-kindness statements, which may amplify the benefits of these interventions.

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.

Face yourself: The social neuroscience of mirror gazing

In philosophical and psychological accounts alike, it has been claimed that mirror gazing is like looking at ourselves as others. Social neuroscience and social psychology offer support for this view by showing that we use similar brain and cognitive mechanisms during perception of both others’ and our own face. I analyse these premises to investigate the factors affecting the perception of one’s own mirror image. I analyse mechanisms and processes involved in face perception, mimicry, and emotion recognition, and defend the following argument: because perception of others’ face is affected by our feelings toward them, it is likely that feelings toward ourselves affect our responses to the mirror image. One implication is that negative self-feelings can affect mirror gazing instantiating a vicious cycle where the negative emotional response reflects a previously acquired attitude toward oneself. I conclude by discussing implications of this view for psychology and social studies.

Features of the Impact of Self-Assessments of the Face on the Emotional Component

The purpose of the study is to determine the hierarchy of the intensity of the impact of the totality of factors (self-assessments of the face, satisfaction, concern about the appearance) on the subjective well-being of the person and its components. At the first stage, the intensity of the impact of concern, satisfaction with the appearance on the self-esteem of the face were determined. The second stage tested the hypothesis of the hierarchical intensity of the effect of three predictors on the subjective assessment of well-being. The study involved 188 people (~35% male and ~65% female) aged 18 to 45 years (M = 26.6), The distribution of study participants by individual self-assessment levels is as follows: ~ 32% have a low and below average level; 34.6% — average level; 33.5% are distinguished by a high and above average level of assessment of their face (c2 = 166.435 at p = 0.000).Methods used: 1. Questionnaire “Self-assessment of appearance,” developed by V.A. Labunskaya. 2. The scale “Subjective well-being” in the adaptation of V.M. Sokolova. 3. Questionnaire “Attitude to its appearance: satisfaction and concern,” developed by E.V. Kapitanova, V.A. Labunskaya. Mathematical procedures: c2, Kraskel-Wallace criteria, Mann-Whitney U, linear and step-by-step regression analysis (IBM SPPS Statistical 23.0).Results of the study: a combination of predictors (satisfaction, concern about the appearance, self-esteem of the face) has a significant but insufficiently intense effect on subjective well-being; face self-esteem affects the most intensely; the expression of satisfaction with the appearance affects only certain components of subjective well-being; concern about the appearance does not affect both the integral subjective assessment of well-being and the assessments of its components.