Simulating drone and bodily movements: a behavioral study
Bibliographic record
Abstract
Introduction: This study explores how drone movements and human bodily gestures influence spectators' perceptions, focusing on physical and emotional involvement, aesthetic appreciation, and time perception. Methods: Inspired by the iconic staircase scene from the Soviet film The Cranes Are Flying (1957), a set of 81 naturalistic video stimuli was created using a drone-mounted camera, varying in Drone Movement (Ascending, Descending, Still), Human Presence (Female, Male, None), and Image Speed (Normal, Low, Very Slow). Participants evaluated each video based on Liking, Perceived Movement, Physical Involvement, Emotional Involvement and Perceived Duration. Results and discussion: Results showed that ascending movements elicited the highest levels of perceived movement, aesthetic appreciation and emotional engagement, outperforming descending and still movements. These results could be explained by a stronger sense of effort and exertion associated with ascending movements, aligning with the embodied simulation of upward motion against gravity. Human presence significantly enhanced ratings across all metrics compared to videos without human figures, thus suggesting that bodily movements play a crucial role in evoking stronger viewer involvement. Additionally, the Female condition received higher aesthetic ratings. Notably, normal image speed yielded greater perceived movement and physical involvement than slowed footage, highlighting a stronger connection to the natural rhythm of bodies in motion. Furthermore, ascending and descending conditions were perceived as lasting longer than still, corroborating prior research on time perception distortions with dynamic stimuli. Correlation analysis highlighted a strong link between physical involvement, emotional engagement, and aesthetic appreciation, underscoring the interplay between bodily and emotional responses. This study emphasizes the potential of drone-based cinematography to evoke embodied and emotional responses, reinforcing the role of embodied simulation theory in cinematic experiences.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".