Biomechanical metrics of aesthetic perception in dance

S. Bronner, James Shippen

    Research output: Contribution to journalArticle

    8 Citations (Scopus)
    34 Downloads (Pure)

    Abstract

    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 développé arabesque in three conditions: (1) slow tempo, (2) slow tempo with relevé, 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 relevé (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.
    Original languageEnglish
    Pages (from-to)3565-3581
    JournalExperimental Brain Research
    Volume233
    Issue number12
    DOIs
    Publication statusPublished - 30 Aug 2015

    Fingerprint

    Esthetics
    Principal Component Analysis
    Gestures
    Brain
    Biomechanical Phenomena
    Linear Models
    Analysis of Variance
    Extremities

    Bibliographical note

    The final publication is available at Springer via http://dx.doi.org/10.1007/s00221-015-4424-4

    Keywords

    • Akaike Information Criteria
    • Chunking
    • Dimensionless jerk
    • Principal component analysis
    • Variability

    Cite this

    Biomechanical metrics of aesthetic perception in dance. / Bronner, S.; Shippen, James.

    In: Experimental Brain Research, Vol. 233, No. 12, 30.08.2015, p. 3565-3581.

    Research output: Contribution to journalArticle

    @article{b82ac81fe1ca45b69822ebdc2a44b26f,
    title = "Biomechanical metrics of aesthetic perception in dance",
    abstract = "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{\'e}velopp{\'e} arabesque in three conditions: (1) slow tempo, (2) slow tempo with relev{\'e}, 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{\'e} (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.",
    keywords = "Akaike Information Criteria, Chunking, Dimensionless jerk, Principal component analysis, Variability",
    author = "S. Bronner and James Shippen",
    note = "The final publication is available at Springer via http://dx.doi.org/10.1007/s00221-015-4424-4",
    year = "2015",
    month = "8",
    day = "30",
    doi = "10.1007/s00221-015-4424-4",
    language = "English",
    volume = "233",
    pages = "3565--3581",
    journal = "Experimental Brain Research",
    issn = "0014-4819",
    publisher = "Springer Verlag",
    number = "12",

    }

    TY - JOUR

    T1 - Biomechanical metrics of aesthetic perception in dance

    AU - Bronner, S.

    AU - Shippen, James

    N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s00221-015-4424-4

    PY - 2015/8/30

    Y1 - 2015/8/30

    N2 - 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 développé arabesque in three conditions: (1) slow tempo, (2) slow tempo with relevé, 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 relevé (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.

    AB - 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 développé arabesque in three conditions: (1) slow tempo, (2) slow tempo with relevé, 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 relevé (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.

    KW - Akaike Information Criteria

    KW - Chunking

    KW - Dimensionless jerk

    KW - Principal component analysis

    KW - Variability

    U2 - 10.1007/s00221-015-4424-4

    DO - 10.1007/s00221-015-4424-4

    M3 - Article

    VL - 233

    SP - 3565

    EP - 3581

    JO - Experimental Brain Research

    JF - Experimental Brain Research

    SN - 0014-4819

    IS - 12

    ER -