Does spatial invariance result from insensitivity to change?
Bibliographic record
Abstract
One of the fundamental unanswered questions in visual science regards how the visual system attains a high degree of invariance (e.g., position invariance, size invariance, etc.) while maintaining high selectivity. Although a variety of theories have been proposed, most are distinguished by the degree to which information is maintained or discarded. To test whether information is maintained or discarded, we have compared the ability of the human visual system to detect a variety of wide-field changes to natural images. The changes range from simple affine transforms and intensity changes common to our visual experience to random changes as represented by the addition of white noise. When sensitivity was measured in terms of the Euclidean distance (L(2) norm) between image pairs, we found that observers were an order of magnitude less sensitive to the geometric transformations than to added noise. A control experiment ruled out that the sensitivity difference was caused by the statistical properties of the image difference created by this transformation. We argue that the remarkable difference in sensitivity relates to the processes used by the visual system to build invariant relationships and leads to the unusual result that observers are least sensitive to those transformations most commonly experienced in the natural world.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".