Learning to see human: universal aspects and cultural variations in the development of anthropomorphism
Bibliographic record
Abstract
Anthropomorphism is the tendency to attribute human characteristics (i.e. emotions, intentions) to nonhuman animals, technologies, and nature. This disposition varies by individual, but there may be factors that contribute to one’s tendencies. The current study investigates cultural contributions to the development of individual anthropomorphism by assessing the anthropomorphic tendencies of children and adults from China and Canada. The Chinese sample included children (4-6 years; N=299) and adults (16-28 years; N=294); the Canadian sample included children (4-6 years; N=103) and adults (17-52 years; N=158). All participants were administered the Individual Differences in Anthropomorphism Questionnaire - Child Form (IDAQ-CF), a 12-item measure assessing individual differences in the tendency to anthropomorphize nonhuman entities. The scale ranges from no attribution to full attribution - children responded on a 4-point scale and adults on a 10-point scale. To test for cultural differences, I ran independent-samples T-tests for both the child and adult samples, and repeated-measures ANOVA, with culture and age group as the between-subjects factors and IDAQ-CF subscales (animals and technology/nature) as the within-subjects factor. Children from China more readily anthropomorphized technology, nature, and animals than children from Canada. Adults from China more readily anthropomorphized technology and nature than those from Canada, but the adult samples did not differ on the animal subscale. The difference on the technology/nature subscale present in the child sample becomes more extreme in adulthood, but the difference in the animal subscale does not persist. It may be that entities that do not provide many cues regarding internal state are more sensitive to cultural conceptions than entities that provide many cues. The study of anthropomorphism has broad implications for how people understand and treat both human and nonhuman others, and it reflects a diversity of worldviews about who or what has emotions, intentions, and conscious minds.
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 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".