| Literature DB >> 29761005 |
Yan Zhang1, Yu Xiang1, Ying Guo2, Lili Zhang1.
Abstract
Introduction: To explore the beauty-related perceptual bias and answers the question: Who can capture the mind of the beholder? Many studies have explored the specificity of human faces through ERP or other ways, and the materials they used are general human faces and other objects. Therefore, we want to further explore the difference between attractive faces and beautiful objects such as flowers.Entities:
Keywords: attractive faces; beautiful flowers; eye tracking; perceptual bias; specificity
Mesh:
Year: 2018 PMID: 29761005 PMCID: PMC5943731 DOI: 10.1002/brb3.945
Source DB: PubMed Journal: Brain Behav Impact factor: 2.708
Participants rated different female faces on five dimensions: attractiveness, joviality, arousal, dominance, and emotion valence. The result means that the main effect of attractive category was significantly different (p < .001), and the post hoc analyses found that there were significant differences between face categories (all p < .01)
| Rating dimension ( | Attractive faces, M ( | Unattractive faces, M ( | Average faces, M ( |
|
|
|---|---|---|---|---|---|
| Attractiveness | 7.92 (0.82) | 2.71 (0.99) | 5.35 (1.33) | 761.141 | .000 |
| Joviality | 6.36 (0.68) | 2.78 (0.60) | 4.36 (0.48) | 879.517 | .000 |
| Arousal | 6.44 (0.71) | 6.32 (0.83) | 6.26 (0.92) | 1.343 | .263 |
| Dominance | 4.70 (0.46) | 4.81 (0.66) | 4.77(0.38) | 0.996 | .370 |
| Emotion valence | 2 | 2 | 2 |
The comparison between flower and female face images. The t test effect of all categories was not significantly different (p > .05), which means that there is no much difference in the face images and flower images in the attractive level
| Rating dimension ( | Faces, M ( | Flowers, M ( |
|
|
|---|---|---|---|---|
| Attractiveness | 6.51 (0.67) | 6.58 (0.74) | −0.463 | .645 |
| Joviality | 6.20 (0.54) | 6.10 (0.51) | 0.794 | .429 |
| Arousal | 6.27 (0.60) | 6.27 (0.71) | −0.008 | .993 |
| Dominance | 4.60 (0.51) | 4.56 (0.41) | 0.442 | .660 |
| Emotion valence | 2 | 2 |
Figure 1Example of experiment material. It is the images of a beautiful female and a flower
The eye movement index on beautiful pictures by different participant
| Picture Type | Gender | APD | TFT | FFDT | TTFF | FC |
|---|---|---|---|---|---|---|
| Face | M | 550.87 ± 175.83 | 692.96 ± 227.14 | 281.82 ± 74.82 | 257.72 ± 81.23 | 2.46 ± 0.69 |
| F | 474.39 ± 236.90 | 758.53 ± 293.73 | 260.50 ± 41.28 | 341.72 ± 151.65 | 2.94 ± 1.09 | |
| Flower | M | 551.37 ± 172.71 | 567.33 ± 195.23 | 268.11 ± 64.56 | 419.72 ± 153.79 | 1.99 ± 0.62 |
| F | 475.28 ± 230.38 | 750.37 ± 426.07 | 270.92 ± 63.53 | 444.71 ± 189.91 | 2.67 ± 1.23 |
There are five eye movement indexes: APD, average pupil diameter; TFT, total fixation time; FFDT, first fixation duration time; TTFF, time to first fixation; FC, fixation count.