Bibliographic record
Abstract
Size constancy was investigated in DF, a patient with visual form agnosia, using a technique based on Emmert's law of visual after-images. DF was first given a task in which she was asked to indicate the distance of a vertical surface and a task where she had to estimate the width of a series of squares (widths ranging from 5 cm to 35 cm) placed at varying distances and having a constant visual angle. In the distance estimation task, DF greatly overestimated the distance of the vertical surface placed in front of her. DF also had great difficulty performing the size estimation task. DF then performed a task in which she stared at a bright 5 cm square for a brief period of time at a distance of 30 cm followed by the presentation of a vertical surface which varied in distance and was asked to indicate the width of the after-image either verbally or manually. DF's after-images conformed to the size-distance relationship predicted by Emmert's law--as the distance of the vertical surface increased her perception of the size of the after-images also increased. These data demonstrate that although DF is rather impaired in tasks that require explicit estimates of size and distance, at some level, DF must have relatively intact size constancy mechanisms given that her estimates of the width of the after-image conform to Emmert's law. Thus, the processes underlying explicit judgements of size and distance appear to differ from those underlying the size and distance scaling of after-images.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".