Jennifer Marie Binzak Fugate

Emotional granularity (EG) has been associated with adaptive emotion regulation, psychological well-being, and reduced psychopathology. The present study examined whether teaching highly specific emotion words would increase EG and improve emotion regulation, emotional dysregulation, and well-being outcomes. A total of 95 adults were randomly assigned to either a 14-day emotion word training condition (n = 48) or a matched control word training condition (n = 47). Before and after training, participants completed assessments of word knowledge, self-reported and behavioral estimates of EG, emotion regulation, emotional dysregulation, and satisfaction with life. Mediation analyses evaluated whether changes in EG explained the relationship between increases in word knowledge variables and positive psychological outcomes. Participants in both emotion and control word conditions showed improvements in word knowledge variables after training, but only those in the emotion word group showed that increased emotion word usage was associated with higher EG (using the Range and Differentiation of Emotion Expression Scale, RDEES) and lower emotional dysregulation and reduced suppression. Those in the control group showed mixed effects for self-reported EG and outcomes. For neither group, however, were any of the effects mediated by either EG measurement. Furthermore, behavioral and self-report measures of EG were not significantly correlated, suggesting that they may capture distinct aspects of emotional functioning. These findings provide preliminary support for emotion vocabulary training as a low-cost intervention that may enhance emotional functioning and suggest that self-perceived EG may be more closely linked to adaptive outcomes.

Mindfulness practices reduce rumination, sharpen emotional clarity, ease anxiety and depressive symptoms, and bolster self-efficacy. Why one broad class of interventions yields such varied benefits — and why the body seems so central to those effects — remains only partially understood. In this theoretical review, we draw on clinical, neuroscientific, and computational evidence to address these questions through the embodied predictive processing (EPP) framework. Mindfulness training reshapes the insula and the broader interoceptive network, while altering connectivity across the salience, default mode, and central executive networks. Within EPP, the brain functions as a prediction engine whose internal models are continually updated through interoception — the primary channel linking the brain to the physiological systems it regulates. We argue that mindfulness works, at least in part, by training the dynamic allocation of precision to interoceptive signals, helping the brain update its predictions more flexibly as demands shift. From this perspective, emotional dysregulation reflects not too much or too little feeling, but difficulty reweighting bodily signals when context changes. We conclude with implications for mindfulness-based interventions in anxiety, depression, eating disorders, and chronic pain.

Simulation has evolved from basic practice to Immersive Extended Reality (I-XR). This systematic review examined 56 published studies on the impact of I-XR, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), on the education of medical and nursing students, specifically their skill competency, and knowledge acquisition. The results demonstrate the significant potential of I-XR in healthcare education, with 42.5% of VR studies, 42.9% of AR studies, and the single MR study also demonstrating greater improvements in clinical skills and knowledge acquisition compared to non-immersive (non-I-XR) training conditions. In contrast, only 2.5% of VR studies and 7.14% of AR studies favored non-I-XR methods. It is important, however, to acknowledge the 26.8% of studies that showed mixed results (some evidence for the I-XR methods on some outcomes, but also some evidence for the non-I-XR methods, on other outcomes). Notably, the review also identified a critical gap in the theoretical foundations of I-XR learning, highlighting the urgent need for research to inform the effective pedagogical implementation of these powerful tools. We offer a preliminary framework to address the lack of learning theory in healthcare I-XR training, with implications for pedagogical practices.

Simulation has evolved from basic practice to Immersive Extended Reality (I-XR). This systematic review examined 56 published studies on the impact of I-XR, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), on the education of medical and nursing students, specifically their skill competency and knowledge acquisition. The results demonstrate the significant potential of I-XR in healthcare education, with 42.5% of VR studies, 42.9% of AR studies, and the single MR study also demonstrating greater improvements in clinical skills and knowledge acquisition compared to non-I-XR training conditions. In contrast, only 2.5% of VR studies and 7.14% of AR studies favored non-I-XR methods. It is important, however, to acknowledge the 26.8% of studies that showed mixed results (some evidence for the I-XR methods on some outcomes, but also some evidence for the non-immersive (non-I-XR) methods, on other outcomes). Notably, the review also identified a critical gap in the theoretical foundations of I-XR learning, highlighting an urgent need for research to inform the effective pedagogical implementation of these powerful tools.

People vary in the precision with which they experience and report on their emotions, known as emotional granularity, and this precision predicts their ability to regulate their emotions. It is not yet known, however, whether links between emotional granularity and emotion regulation are due to variation in knowledge of emotion words—specifically, individuals’ reported usage, understanding, and ability to accurately define emotion words. In the present report, we combined data from six studies to address this gap in the literature using an integrative data analysis. Participants across the studies reported on how often they used and how well they understood a list of precise emotion words, and were tested on whether they could correctly pick a definition for each. They also completed questionnaire measures of self-reported emotional granularity (differentiation) and emotion dysregulation. Emotion word accuracy and understanding were highly correlated, so individual models were tested each separately to predict emotion dysregulation. In the model including usage and understanding, we observed a main effect of understanding, such that participants with greater self-reported understanding of emotion words reported less difficulty regulating their emotions. Similar effects were found for the model including usage and accuracy, such that individuals with higher emotion word accuracy had less difficulty regulating their emotions. Critically, these findings held when accounting for self-reported granularity (differentiation), suggesting that measures of emotion word knowledge have value for predicting emotion regulatory outcomes. Future work should examine whether individuals’ emotion word knowledge is also linked to mental health outcomes.

Editorial: The role of emotional granularity in emotional regulation, mental disorders, and well-being

It is long hypothesized that there is a reliable, specific mapping between certain emotional states and the facial movements that express those states. This hypothesis is often tested by asking untrained participants to pose the facial movements they believe they use to express emotions during generic scenarios. Here, we test this hypothesis using, as stimuli, photographs of facial configurations posed by professional actors in response to contextually-rich scenarios. The scenarios portrayed in the photographs were rated by a convenience sample of participants for the extent to which they evoked an instance of 13 emotion categories, and actors’ facial poses were coded for their specific movements. Both unsupervised and supervised machine learning find that in these photographs, the actors portrayed emotional states with variable facial configurations; instances of only three emotion categories (fear, happiness, and surprise) were portrayed with moderate reliability and specificity. The photographs were separately rated by another sample of participants for the extent to which they portrayed an instance of the 13 emotion categories; they were rated when presented alone and when presented with their associated scenarios, revealing that emotion inferences by participants also vary in a context-sensitive manner. Together, these findings suggest that facial movements and perceptions of emotion vary by situation and transcend stereotypes of emotional expressions. Future research may build on these findings by incorporating dynamic stimuli rather than photographs and studying a broader range of cultural contexts.

Emoji faces, which are ubiquitous in our everyday communication, are thought to resemble human faces and aid emotional communication. Yet, few studies examine whether emojis are perceived as a particular emotion and whether that perception changes based on rendering differences across electronic platforms. The current paper draws upon emotion theory to evaluate whether emoji faces depict anatomical differences that are proposed to differentiate human depictions of emotion (hereafter, “facial expressions”). We modified the existing Facial Action Coding System (FACS) (Ekman and Rosenberg, 1997) to apply to emoji faces. An equivalent “emoji FACS” rubric allowed us to evaluate two important questions: First, Anatomically, does the same emoji face “look” the same across platforms and versions? Second, Do emoji faces perceived as a particular emotion category resemble the proposed human facial expression for that emotion? To answer these questions, we compared the anatomically based codes for 31 emoji faces across three platforms and two version updates. We then compared those codes to the proposed human facial expression prototype for the emotion perceived within the emoji face. Overall, emoji faces across platforms and versions were not anatomically equivalent. Moreover, the majority of emoji faces did not conform to human facial expressions for an emotion, although the basic anatomical codes were shared among human and emoji faces. Some emotion categories were better predicted by the assortment of anatomical codes than others, with some individual differences among platforms. We discuss theories of emotion that help explain how emoji faces are perceived as an emotion, even when anatomical differences are not always consistent or specific to an emotion.