"It's Godzilla!": Body-part compatibility in mammalians and reptiles
Bibliographic record
Abstract
The purpose of the present study was to determine how humans code homologous body parts of nonhuman mammal and reptilian animals with respect to the representation of the human body. To this end, participants completed a compatibility task while viewing static images of mammalian and reptilian animals. Participants completed thumb-press or foot-pedal responses to red or blue targets, respectively, that appeared over different meerkat and lizard images in bipedal and quadrupedal postures. A head condition was added to assess any possible vertical compatibility effects. The results support the notion that the limbs of nonhuman mammalian animals are mapped onto the human body schema when the mammal is in a bipedal posture. However, an inconsistent pattern of limb compatibility effects was observed in the reptilian condition when in a bipedal posture. Finally, a nonhuman body representation is referenced when observing both nonhuman mammals and reptiles in a typical (quadrepedal) animal stance. It can be concluded that differences in body representation exist when observing mammalian and reptilian forms in both bipedal and quadrupedal postures. In addition, this pattern of results suggests that the bipedal body representation may be class-specific and lends support to the idea of a graded level of activation within the extrastriate body area associated with body plans that are similar to that of humans. Acknowledgments: This work was supported by operating grants from the Natural Sciences and Engineering Research Council of Canada and the Ontario Ministry of Research and Innovation.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".