Awe is a complex emotion composed of an appraisal of vastness and a need for accommodation. The purpose of this study was to develop a robust state measure of awe, the Awe Experience Scale (AWE-S), based on the extant experimental literature. In study 1, participants (N = 501) wrote about an intense moment of awe that they had experienced and then completed a survey about their experience. Exploratory factor analysis revealed a 6-factor structure, including: altered time perception (F1); self-diminishment (F2); connectedness (F3); perceived vastness (F4); physical sensations (F5); need for accommodation (F6). Internal consistency was strong for each factor (α ≥ .80). Study 2 confirmed the 6-factor structure (N = 636) using fit indices (CFI = .905; RMSEA = .054). Each factor of the AWE-S is significantly correlated with the awe items of the modified Differential Emotions Scale (mDES) and Dispositional Positive Emotion Scale (D-PES). Triggers, valence, and themes associated with awe experiences are reported.

Awe, a complex emotion composed by the appraisal components of vastness and need for accommodation, is a profound and often meaningful experience. Despite its importance, psychologists have only recently begun empirical study of awe. At the experimental level, a main issue concerns how to elicit high intensity awe experiences in the lab. To address this issue, Virtual Reality (VR) has been proposed as a potential solution. Here, we considered the highest realistic form of VR: immersive videos. 42 participants watched at immersive and normal 2D videos displaying an awe or a neutral content. After the experience, they rated their level of awe and sense of presence. Participants’ psychophysiological responses (BVP, SC, sEMG) were recorded during the whole video exposure. We hypothesized that the immersive video condition would increase the intensity of awe experienced compared to 2D screen videos. Results indicated that immersive videos significantly enhanced the self-reported intensity of awe as well as the sense of presence. Immersive videos displaying an awe content also led to higher parasympathetic activation. These findings indicate the advantages of using VR in the experimental study of awe, with methodological implications for the study of other emotions.

Technology now makes it possible to understand efficiently and at large scale how people use language to reveal their everyday thoughts, behaviors, and emotions. Written text has been analyzed through both theory-based, closed-vocabulary methods from the social sciences as well as data-driven, open-vocabulary methods from computer science, but these approaches have not been comprehensively compared. To provide guidance on best practices for automatically analyzing written text, this narrative review and quantitative synthesis compares five predominant closed- and open-vocabulary methods: Linguistic Inquiry and Word Count (LIWC), the General Inquirer, DICTION, Latent Dirichlet Allocation, and Differential Language Analysis. We compare the linguistic features associated with gender, age, and personality across the five methods using an existing dataset of Facebook status updates and self-reported survey data from 65,896 users. Results are fairly consistent across methods. The closed-vocabulary approaches efficiently summarize concepts and are helpful for understanding how people think, with LIWC2015 yielding the strongest, most parsimonious results. Open-vocabulary approaches reveal more specific and concrete patterns across a broad range of content domains, better address ambiguous word senses, and are less prone to misinterpretation, suggesting that they are well-suited for capturing the nuances of everyday psychological processes. We detail several errors that can occur in closed-vocabulary analyses, the impact of sample size, number of words per user and number of topics included in open-vocabulary analyses, and implications of different analytical decisions. We conclude with recommendations for researchers, advocating for a complementary approach that combines closed- and open-vocabulary methods. (PsycInfo Database Record (c) 2021 APA, all rights reserved).