Symmetry perception: a high-density ERP approach
Bibliographic record
Abstract
Symmetry is a salient characteristic of visual stimuli that may be used to detect and recognize many natural and manufactured objects. Psychophysical studies have demonstrated that we are extremely fast and efficient at extracting symmetry, but the neural mechanisms underlying symmetry processing remain largely unknown. In one of very few ERP studies, Norcia et al. (JOV 2002) examined the time course of symmetry processing. In their experiment, observers were presented with symmetric and random dot patterns alternating every 500 ms. ERPs measured with symmetric-random sequences diverged from control random-random sequences between 130 and 220 ms after stimulus onset. However, it is not clear whether this response difference was due to symmetry per se or to the presence of structure in the symmetric dot patterns. We tested this possible confound by adding textured dot patterns (Glass patterns), which were asymmetric yet clearly distinguishable from random dot patterns, to the same experimental design. EEG was recorded from 256 electrodes while observers passively viewed alternating random-texture, random-symmetric, and random-random stimuli. Our preliminary results replicate and extend the findings of Norcia et al.: ERPs to structured dot textures were very similar to those evoked by random dots, and both begin to differ from ERPs to symmetric dots at about 160–170 ms after stimulus onset. We note that the onset of the symmetry effect is relatively late compared to the time course of object processing as described in the ERP literature. To further investigate this issue we will conduct another experiment in which observers actively discriminate between symmetric and non-symmetric patterns. This will allow us to relate the behavioral RTs with the onset of the symmetry ERP effects. Source analyses on individual subjects will also be conducted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".