Contrapposto posture captures visual attention: An online gaze tracking experiment
Bibliographic record
Abstract
Goddesses of love and beauty are frequently depicted in artwork in a contrapposto posture with one leg relaxing while the other bears the weight. Previous research has indicated that compared to an upright standing pose, a contrapposto pose is considered more attractive with its curviness capturing greater visual attention. Yet, whether a body posed in contrapposto is generally more visually attention-grabbing than an upright body remains unknown. We sought to address this gap and also examined if individual differences in sociosexuality – individual differences in willingness to engage in uncommitted sexual relations – influence attentional allocation. Online gaze-tracking was employed to monitor subjects (n = 71) during image presentation in a preferential looking design (contrapposto verse standing). Participants had a greater proportion of their gaze directed towards female bodies depicted in contrapposto pose compared to a standing posture over an extended period of time but not in the first gaze shift. Moreover, sociosexuality correlated positively with the proportion of gazes towards contrapposto stimuli but fell short of statistical significance. The results of the current study indicate that top-down factors play a role in how people allocate more attention to contrapposto poses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".