Experiencing beauty in everyday life

Beauty surrounds us in many ways every day. In three experience sampling (ESM) studies we investigated frequency, category of eliciting stimuli (natural vs human-made) and, the potential moderating role of several individual difference measures on such everyday experiences of beauty in an ecologically valid manner. Further, we explored the impact of such experiences on valence & arousal. Study 1 re-analysed data from a previous study, in line with the current aims. In Studies 2 and 3, we asked participants to report daily experiences of beauty using a mixed random and event-contingent sampling schedule. Mobile notifications (random sampling) prompted participants to take a photo and rate the beauty of their surroundings. Further, current valence and arousal were assessed. Notification frequency and total days of participation differed between these two studies. Participants were able to report additional experiences outside of the notification windows (event-contingent sampling). Our results indicate that we frequently encounter beauty in everyday life and that we find it in nature, in particular. Our results further suggest a mood-boosting effect of encounters with beauty. Lastly, our results indicate influences of individual differences however, these were inconclusive and require further attention.


Supplementary Figure
Table of 1) Response Behaviour in the ESM part of Study 2 and 3, and 2) Responses to the post-ESM questionnaire. .

Supplementary Table 4. Study
2: Nature and City Relatedness Model.In contrast to the aesthetics model, the nature and city relatedness model did not significantly improve the model fit compared to the 'category model' (χ2 = 8.5, df = 14, p = 0.86).

Table 5 .
Study 2: Personality Model.The inclusion of the personality facets as predictors significantly improved the model fit compare to the 'category model' (χ2 = 101.28,df = 62, p = 0.001).Openness to Experience N = 84, as one person seems to have skipped an item in the BFI.

Table 6 .
Individual Difference Measures.Mean scores and standard deviations for all individual difference measures and their subscales are shown for both Study 2 (left) and 3 (right).

Table 7 .
Study 2: Results of the two multilevel models investigating the influence of experiences of beauty on the two mood measures (i.e.valence and arousal).Compared to their respective null models (i.e.only including participant and day as random intercepts), inclusion of beauty as a fixed effect term improves model fit of the valence (

Table 8 .
Study 3: Results of the multilevel model investigating the influence of category (i.e.human-made vs. natural) on experiences of beauty.Including category as a predictor significantly improved the fit of the model as compared to the null model (χ2 = 490.22,df = 1, p < 0.001).

Table 9 .
Study 3: Results of the multilevel model investigating the influence of category (i.e.human-made vs. natural) and repetition on experiences of beauty.Including repetition additionally improved the model fit compared to the model including only category (χ2 = 8.36, df = 2, p = 0.015)

Table 10 .
Study 3: Aesthetics Model.Model fit was not improved over that of the category only model (χ2 = 16.90, df = 14, p = 0.261).Removing EBS from the model, as here it does not even appear relevant in 3-or 4-way interactions as it did in study 2, did not significantly improve model fit either.