Viewpoint and Orientation Influence Picture Recognition in the Blind
Bibliographic record
Abstract
In the first three experiments, subjects felt solid geometrical forms and matched raised-line pictures to the objects. Performance was best in experiment 1 for top views, with shorter response latencies than for side views, front views, or 3-D views with foreshortening. In a second experiment with blind participants, matching accuracy was not significantly affected by prior visual experience, but speed advantages were found for top views, with 3-D views also yielding better matching accuracy than side views. There were no performance advantages for pictures of objects with a constant cross section in the vertical axis. The early-blind participants had lower performance for side and frontal views. The objects were rotated to oblique orientations in experiment 3. Early-blind subjects performed worse than the other subjects given object rotation. Visual experience with pictures of objects at many angles could facilitate identification at oblique orientations. In experiment 5 with blindfolded sighted subjects, tangible pictures were used as targets and as choices. The results yielded superior overall performance for 3-D views (mean, M = 74% correct) and much lower matching accuracy for top views as targets (M = 58% correct). Performance was highest when the target and matching viewpoint were identical, but 3-D views (M = 96% correct) were still far better than top views. The accuracy advantage of the top views also disappeared when more complex objects were tested in experiment 6. Alternative theoretical implications of the results are discussed.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".