Bibliographic record
Abstract
The body inversion effect is the finding that inverted body posture pictures are more difficult to recognize than upright body posture pictures are. The present study reinvestigated the body inversion effect in human observers using behavioral and eye movement measures to explore whether the body inversion effect correlates with specific eye movement features. Results showed that body postures elicited a robust and stable body inversion effect in reaction time throughout the experimental sessions. Eye-tracking data showed that the body inversion effect was robust only in the first fixation duration, but not in the second fixation duration. The analysis of the regions of interest showed that most fixations were located in the upper body for both the upright and the inverted body postures. Compared with inverted body postures, the upright postures led to a shorter reaction time and a shorter first fixation duration, but a larger portion of time to fixate on the head region, suggesting that participants tended to use head as a reference point to process upright body postures. For both the behavioral and the eye movement measures, the body inversion effect was robust for biomechanically possible body postures. However, for biomechanically impossible body postures (with angular manipulation of two joints), the effect was mixed. Although the error rate failed to show the body inversion effect, the reaction time measure and most eye movement measures, however, showed a body inversion effect. Overall, these results suggested that upright body postures are processed in expertise recognition and are processed configurally by human observers.
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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".