PARIETAL CONTRIBUTIONS TO ABSTRACT NUMEROSITY MEASURED WITH STEADY STATE VISUAL EVOKED POTENTIALS
Bibliographic record
Abstract
Abstract Non-symbolic number changes produce transient Event Related Potentials over parietal electrodes, while numerosity effects measured with Steady-State Visual Evoked Potentials (SSVEPs) appear to originate in occipital cortex. We hypothesized that the stimulation rates used in previous SSVEP studies may be too rapid to drive parietal numerosity mechanisms. Here we recorded SSVEPs and behavioral reports over a slower range of temporal frequencies than previously used. Isoluminant dot stimuli updated at a consistent “carrier” frequency (3-6 Hz) while periodic changes in numerosity (e.g. 8→5) formed an even slower “oddball” frequency (0.5-1 Hz). Each numerosity oddball condition had a matched control condition where the number of dots did not change. Carrier frequencies induced SSVEPs with midline occipital topographies that did not differentiate the presence or absence of numerosity oddballs. By contrast, SSVEPs at oddball frequencies had parietal topographies and responded more strongly when oddballs were present. Consistent with our hypothesis, numerosity effects were stronger at slower stimulation rates. In a second study, the numerosity change was either supra-threshold (e.g. 8→5 dots) or near the threshold required for detecting numerosity changes (e.g. 8→9 dots). We found robust parietal responses for the supra-threshold case only, indicating a numerical distance effect . A third study replicated the parietal oddball SSVEP effect across four distinct suprathreshold numerosity-change conditions and showed that number change direction does not influence the effect. These findings show that SSVEP oddball paradigms can probe parietal computations of abstract numerosity, and may provide a rapid, portable approach to quantifying number sense within educational settings.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".