ABSTRACT
Previous studies suggested that (1) facial attractiveness perception can be increased with facial skin homogeneity improving; and (2) human's facial change detection increases along with facial skin homogeneity increases. However, it's unknown whether a face can be perceived prettier than it did before while still being considered as physically the same. It is possible that these two kinds of cognitive-aesthetic processing may have separate mathematical functions in psychophysical studies. In other words, human's facial attractiveness differentiation may be more sensitive than facial change detection. In this current study, we explored the above questions. Using three types of psychophysical techniques to manipulate facial skin homogeneity, we measured how participants' sensitivity to facial skin homogeneity and attractiveness change. Results showed a linear function curve for facial physical change detection and a logarithmic function curve was drawn in the forced-choice technique, which was the most sensitive one, indicating that participants can judge a face prettier than before without being aware of it has physically changed. Besides, two linear function curves were shown in the same/different technique and a rating technique. Taken together, this current study revealed that facial attractiveness can be enhanced and discriminated by improving facial skin homogeneity, without being realized by people with conscious awareness that the face has been changed.
ABSTRACT
OBJECTIVE: To explore the response of electroencephalography (EEG) signal to biceps brachii fatiguing induced by sustained isometric (non-maximal) voluntary contraction (non-MVC) and to lay a foundation for effective and reliable fatigue evaluation system and for understanding fatigue mechanisms. METHODS: Ten male undergraduates performed two different isometric fatiguing tasks with the elbow flexion muscles under a load of 5 and 10 kg respectively, and synchronously EEG signals were collected from scalp. Then amplitude, spectral and Lempel-Ziv complexity C(n) of EEG were calculated. RESULTS: The average EEG(AEEG) increased while the average mean power frequency (MPF) and complexity of EEG decreased in muscle fatigue. Furthermore, this phenomenon was distributed evenly across the entire scalp, but load only influenced AEEG, MPF and C(n) at Fz. CONCLUSION: AEEG of EEG increases generally while MPF and C(n) increase generally during local muscle fatigue. This pattern of EEG reflects the central mechanism of muscle fatigue. AEEG, MPF and C(n) at prefrontal area show load dependence.
