Bibliographic record
Abstract
Aim: Mirror symmetry is a ubiquitous feature in visual scenes, especially in biological objects, and is believed to be encoded by specialized visual mechanisms. Here we examine whether perceived mirror-symmetry is susceptible to adaptation like other basic visual features. Method: Stimuli consisted of random-dot patterns of low dot density that were mirror symmetric about the vertical axis. We manipulated the amount of mirror symmetry by adding variable amounts of random jitter to the dot elements from their baseline positions. The amount of positional jitter determined the degree of randomness. Observers adapted to a pair of patterns in which one was perfectly symmetric and the other random, and the positions of the dot elements were randomly changed every half second during adaptation. Observers varied the relative amount of positional jitter in two subsequently presented test patterns with a mean intermediate amount of symmetry, using a conventional staircase procedure, until a PSE (point-of-subjective-equality) in perceived symmetry was reached. The size of the after-effect was measured as the difference in positional jitter between the two test patterns at the PSE. Results: We found that the perceived symmetry of the test patterns was reduced following adaptation to a perfectly symmetric pattern. Adaptation appeared to only cause the test pattern to look less symmetric, suggesting that symmetry adaptation is unidirectional. Conclusion: Mirror-symmetry is an adaptable feature in human vision that produces a 'symmetry after-effect', or SAE. The effect cannot be due to increased positional uncertainty of the dots caused by adaptation, as both of the test patterns were subject to the same amount of any positional adaptation. The SAE is therefore likely caused by adaptation of mechanisms sensitive to mirror-symmetry. Meeting abstract presented at VSS 2014
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".