| Literature DB >> 34511957 |
Kate Goldie1, David Cumming2,3, Daria Voropai1, Afshin Mosahebi4,5, Sabrina Guillen Fabi6,7, Claus-Christian Carbon8,9.
Abstract
BACKGROUND: To date, the process of adaptation in the setting of aesthetic medicine has not been investigated. The combination of complex advanced feedback in the current intense social media milieu, in conjunction with easily accessible and effective aesthetic treatments, has produced pockets of overtreated patients and over-zealous practitioners. We examine whether aesthetic assessments of attractiveness and what appears natural can be distorted by the cognitive process of adaptation.Entities:
Keywords: adaptation; aesthetic assessments; attractiveness; facial attractiveness; flexibility; norm; standards
Year: 2021 PMID: 34511957 PMCID: PMC8424431 DOI: 10.2147/CCID.S305976
Source DB: PubMed Journal: Clin Cosmet Investig Dermatol ISSN: 1178-7015
Figure 1Lip fullness was manipulated using the Face Liquify tool of Adobe Photoshop and ranged from (A) thinnest possible lips variant (minus 100) to (B) fullest possible lips variant (plus 100).
Figure 2Comparison of presented levels of lip fullness offered by the graphics software (x-axis: from −100 to +100) with the participants’ assessments of lip fullness at the end of the experiment (y-axis: from 1 to 7). Pearson R coefficient for both experimental conditions indicate close to perfect fits. Confidence intervals (CI-95%) are additionally given by shadowed confidence bands (note: the band can hardly be perceived as it is in fact very narrow due to the near-to-perfect fit).
Figure 3Mean data for face attractiveness (left) and face naturalness (right) for both adaptation conditions (top: Adapt_FullLips, bottom: Adapt_ThinLips), split by test phases (black: T1, red: T2). Data is modelled by second-degree polynomial functions—determination coefficient expressed as squared Pearson’s R is given for each curve fitting. Confidence intervals (CI-95%) are additionally given by shadowed confidence bands.
Optimal Lip Fullness That Corresponds to the Mean (M) Maximum Values of the Employed Second-Order Polynomial Models for Each Participant, Adaptation Condition and Test Phase, Calculated for Both Dependent Variables (Attractiveness and Naturalness), Separately
| Attractiveness | ||||
| | 7.63 (23) | 14.04 (24) | +6.40 | |
| | 17.84 (21) | 2.99 (18) | −14.84 | −0.541 (medium) |
| Naturalness | ||||
| | −6.69 (22) | −3.69 (23) | +3.00 | 0.256 (small), |
| | −5.33 (23) | −5.43 (23) | −0.10 |
Notes: The n of the first two columns provides the number of individual datasets that could be modelled. The last two columns show the differences (and direction of difference) between mean data for T2 and T1, expressed by mean differences and the respective effect size (Cohen’s d). Non-significant effects are indicated by usage of italics for the respective effect sizes. Qualification of effect sizes are added in parentheses following the suggestions of Cohen (1988)30.
Figure 4Mean values of individual optimal lip fullness for reaching a maximum for the respective dependent variables attractiveness (left) and naturalness (right). Solid lines show adaptation condition Adapt_FullLips and dashed lines the respective data for Adapt_ThinLips. Error bars indicate ±1 standard error of the mean.