Ensemble representation for animacy: Effects of shape and category
Bibliographic record
Abstract
Humans are highly efficient in identifying individuals or ensembles of animals and man-made objects. For single items, although animacy information could be extracted based on low- or mid-level visual shape features (e.g., animals tend to be curvy and man-made objects are often elongated, Long et al., 2017), category selectivity for individual animals and man-made objects remains after such visual differences are minimized across the categories (e.g., He et al., 2020). For ensemble perception, it remains unclear whether the rapid extraction of animacy across multiple complex, real-world animals or man-made objects may depend on naturalistic variations in low- or mid-level visual features, or category information, between the two categories. To minimize low- or mid-level visual differences among the categories, we used grayscale images of real-world animals and man-made objects, either of round or elongated shapes, which shared comparable gist statistics across categories. Across two experiments, participants judged the relatively numerosity on briefly presented (500ms) displays of six animal and man-made object images of the same shape (round/elongated), with either a numerosity ratio of 4:2 or 5:1. The items were presented either at six fixed locations (Experiment 1, N=43) or six random (among 12) locations (Experiment 2, N=44). We found evidence of ensemble processing for both categories, with significantly higher accuracy in numerosity judgment, compared with the respective baselines for each of the numerosity ratios, calculated based on the expected performance if participants randomly subsampled only one item from a display. Moreover, ensemble perception for animals and man-made objects appears to be facilitated by shape information, with significantly better and faster ensemble performance for round than elongated animals, and for elongated than round man-made objects. These results suggest that ensemble processing of animacy can depend on rapid extraction of category information, and is facilitated by the expected shapes of the respective categories.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".