The influence of visual and nonvisual attributes in visual object identification
Bibliographic record
Abstract
To elucidate the role of visual and nonvisual attribute knowledge on visual object identification, we present data from three patients, each with visual object identification impairments as a result of different etiologies. Patients were shown novel computer-generated shapes paired with different labels referencing known entities. On test trials they were shown the novel shapes alone and had to identify them by generating the label with which they were formerly paired. In all conditions the same triad of computer-generated shapes were used. In one condition, the labels (banjo, guitar, violin) referenced entities that were both visually similar and similar in terms of their nonvisual attributes within semantics. In separate conditions we used labels (e.g., spike, straw, pencil or snorkel, cane, crowbar) that referenced entities that were similar in terms of their visual attributes but were dissimilar in terms of their nonvisual attributes. The results revealed that nonvisual attribute information profoundly influenced visual object identification. Our patients performed significantly better when attempting to identify shape triads whose labels referenced objects with distinct nonvisual attributes versus shape triads whose labels referenced objects with similar nonvisual attributes. We conclude that the nonvisual aspects of meaning must be taken into consideration when assessing visual object identification impairments.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".