Say hello to my little friend: The anthropomorphization of starface
Bibliographic record
Abstract
To understand the actions of other individuals, we must first understand their limbs and how they can be represented. The current study investigated how we perceive the limbs of non-human animals and, specifically, to deduce the aspects of a non-human image that facilitate human-animal representation matching (anthropomorphization). Previous studies revealed that anthropomorphization is influenced by the posture and class of the animal. These studies used a body-part compatibility task as a probe of self-other matching. Self-other matching is indicated by lower response times to targets that are presented on limbs of the image that are homologous to the actors' responding limb (e.g., RTs are lower for hand responses when targets are on the forelimb than the hindlimb of an animal). The current study employed the body-part compatibility task in which hand and foot responses were executed to targets on the limbs of a non-human image (line drawing of a starfish) in three blocks; without a face, with a face (by rearranging basic visual features) and again without a face. Results revealed that there were no compatibility effects when stimuli were presented on the "limbs" of a drawn starfish in the first block. When facial features were added to the starfish, however, significant compatibility effects emerged. Finally, when the face was removed from the image in the third block, the body-part compatibility effect did not significantly persist. Overall, this study provides preliminary evidence on the use of facial features on non-human images to act as a pre-requisite to anthropomorphization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.006 | 0.001 |
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; both teacher heads agree on what is shown here.
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".