Biomechanical metrics of aesthetic perception in dance

被引:39
作者
Bronner, Shaw [1 ,2 ]
Shippen, James [3 ]
机构
[1] ADAM Ctr, Brooklyn, NY 11215 USA
[2] Yale Univ, Sch Med, Dept Psychiat, Brain Funct Lab, New Haven, CT 06511 USA
[3] Coventry Univ, Dept Ind Design, Coventry CV1 5FB, W Midlands, England
关键词
Akaike Information Criteria; Chunking; Dimensionless jerk; Principal component analysis; Variability; SEGMENTAL POSTURAL COORDINATION; TRUNK COORDINATION; MOTOR CONTROL; MOVEMENT VARIABILITY; PERFORMING ART; SMOOTHNESS; MOTION; MUSIC; BRAIN; TASK;
D O I
10.1007/s00221-015-4424-4
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The brain may be tuned to evaluate aesthetic perception through perceptual chunking when we observe the grace of the dancer. We modelled biomechanical metrics to explain biological determinants of aesthetic perception in dance. Eighteen expert (EXP) and intermediate (INT) dancers performed d,velopp, arabesque in three conditions: (1) slow tempo, (2) slow tempo with relev,, and (3) fast tempo. To compare biomechanical metrics of kinematic data, we calculated intra-excursion variability, principal component analysis (PCA), and dimensionless jerk for the gesture limb. Observers, all trained dancers, viewed motion capture stick figures of the trials and ranked each for aesthetic (1) proficiency and (2) movement smoothness. Statistical analyses included group by condition repeated-measures ANOVA for metric data; Mann-Whitney U rank and Friedman's rank tests for nonparametric rank data; Spearman's rho correlations to compare aesthetic rankings and metrics; and linear regression to examine which metric best quantified observers' aesthetic rankings, p < 0.05. The goodness of fit of the proposed models was determined using Akaike information criteria. Aesthetic proficiency and smoothness rankings of the dance movements revealed differences between groups and condition, p < 0.0001. EXP dancers were rated more aesthetically proficient than INT dancers. The slow and fast conditions were judged more aesthetically proficient than slow with relev, (p < 0.0001). Of the metrics, PCA best captured the differences due to group and condition. PCA also provided the most parsimonious model to explain aesthetic proficiency and smoothness rankings. By permitting organization of large data sets into simpler groupings, PCA may mirror the phenomenon of chunking in which the brain combines sensory motor elements into integrated units of behaviour. In this representation, the chunk of information which is remembered, and to which the observer reacts, is the elemental mode shape of the motion rather than physical displacements. This suggests that reduction in redundant information to a simplistic dimensionality is related to the experienced observer's aesthetic perception.
引用
收藏
页码:3565 / 3581
页数:17
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