Translations: Effects of Viewpoint, Feature, and Naming on Identifying Repeatedly Copied Drawings
Bibliographic record
Abstract
We explored the tension between bottom-up and top-down contributions to object recognition in a collaboration between a visual artist and a cognitive psychologist. Initial pictorial renderings of objects and animals from various viewpoints were iteratively copied, and a series of drawings that changed from highly concrete images into highly abstract images was produced. In drawing identification in which sets were shown in reverse order, participants were more accurate, more confident, and quicker to correctly identify the evolving image when it was originally displayed from a canonical viewpoint with all salient features present. In drawing identification in which images were shown in random order, more abstract images could be resolved as a result of previously identifying a more concrete iteration of the same drawing. The results raise issues about the influence of viewpoint and feature on the preservation of pictorial images and about the role of labelling in the interpretation of ambiguous stimuli. In addition, the study highlights a procedure in which visual stimuli can degrade without necessitating a substantial loss of complexity.
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 | 0.000 | 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) | 0.000 | 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".