Moving Me, Moving You: Emotional Expressivity, Empathy, and Prior Experience Shape Whole-Body Movement Preferences
Bibliographic record
Abstract
Aesthetics shape and color almost every aspect of our daily lives, from the products we interact with and the clothes we wear to the design of our homes and cities. However, many people associate aesthetics with art, and an historical academic interest in the factors that shape the experience of engaging with art has yielded rich insights into our understanding of the value and ubiquity of empirical aesthetics. While most existing research has focused on music and the visual arts, there is a growing interest in the aesthetics of human movement among empirical aesthetics researchers. In the present study, we sought to examine how individual differences in global empathy and previous movement experience influence aesthetic evaluations of dance sequences. Observers (N = 55) completed a self-report measure of global empathy (Toronto Empathy Questionnaire), provided an assessment of their prior dance experience (via the Goldsmith's Dance Sophistication Index) and rated a series of whole-body point-light display movements (imbued with happiness, sadness, anger, fear, and nonexpressive neutrality) from the McNorm Library (Smith & Cross, 2023) in terms of beauty and liking on 100-point slider scales. Participants demonstrated a general preference for emotionally expressive movement sequences, while specific types of emotional expressivity influenced liking, but not beauty, judgments. Additionally, differences in both prior dance experience and levels of global empathy influenced aesthetic evaluations of the McNorm Library dance clips. We consider the implications of these results for empirical aesthetics and social perception research and discuss how empirical aesthetics research in this area may be of interest, or use, to dance practitioners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".