A surface-based code contributes to visual shape perception
Bibliographic record
Abstract
Considerable uncertainty remains regarding the types of features human vision uses for shape representation. Visual-search experiments are reported which assessed the hypothesis of a surface-based (i.e., edge-bounded polygons) code for shape representation in human vision. The results indicate slower search rates and/or longer response times when the target shape shares its constituent surfaces with distractors (conjunction condition) than when the target surfaces are unique in the display (nonconjunction condition). This demonstration is made using test conditions that strictly control any potential artifact pertaining to target-distractor similarity. The surface-based code suggested by this surface-conjunction effect is strictly 2-D, since the effect occurs even when the surfaces are shared between the target and distractors in the 2-D image but not in their 3-D instantiation. Congruently, this latter finding is unaltered by manipulations of the richness of the depth information offered by the stimuli. It is proposed that human vision uses a 2-D surface-based code for shape representation which, considering other key findings in the field, probably coexists with an alternative representation mode based on a type of structural description that can integrate information pertaining to the 3-D aspect of shapes.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".