Bibliographic record
Abstract
We compared the detection of three different types of symmetry in a visual search paradigm: (i) mirror symmetry, i.e., reflection around a vertical axis, (ii) radial symmetry, i.e., rotations around a centre, and (iii) translational symmetry, i.e., horizontally shifted repetitions. Observers located a single patch containing symmetric dots among varying numbers of distractor patches containing random dots. We used a blocked present/absent protocol and recorded both search times and accuracy. Search times for mirror- and radial-symmetry increased significantly with the number of distractors, but with the translational patterns search slopes were close to zero. Fourier analysis revealed that, as with images of natural scenes, the structural information in both mirror- and radial-symmetric patterns is carried by the phase spectrum. For translational patterns on the other hand the structural information is carried by the amplitude spectrum, consistent with previous analyses of perfectly regular dot patterns. Further analysis revealed that while the mirror and radial patterns produced an approximately Gaussian shaped energy response profile as a function of spatial frequency, the translational pattern profiles contained a distinctive spike, whose magnitude corresponded to the number of repeating sectors. We hence propose distinct mechanisms for the detection of different types of symmetry. A mechanism that utilises phase information, i.e., the spatial relationships among the dots, used to detect the mirror- and radial-symmetric patterns. On the other hand a pre-attentive mechanism that utilises amplitude information, for example the pattern of energy across spatial frequency, is responsible for the detection of translational symmetry and explains why translational symmetry is a pop-out feature. Meeting abstract presented at VSS 2018
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".