Detection and discrimination of flicker contrast in migraine
Bibliographic record
Abstract
AIMS: Flickering light is strongly aversive to many individuals with migraine. This study was designed to evaluate other abnormalities in the processing of temporally modulating visual stimulation. METHODS: We measured psychophysical thresholds for detection of a flickering target and for the discrimination of suprathreshold flicker contrasts (increment thresholds) in 14 migraineurs and 14 healthy controls with and without prior adaptation to high-contrast flicker. Visual discomfort (aversion) thresholds were also assessed. RESULTS: In the baseline (no adaptation) conditions, detection and discrimination thresholds did not differ significantly between groups. Following adaptation, flicker detection thresholds were elevated equivalently in both groups; however, discrimination thresholds were more strongly affected in migraineurs than in controls, showing greater elevation at moderate contrasts and greater threshold reduction (sensitisation) at high contrast (70%). Migraineurs also had significantly elevated discomfort scores, and these were significantly correlated with number of years with migraine. DISCUSSION: We conclude that visual flicker not only causes discomfort but also exerts measurable effects on contrast processing in the visual pathways in migraine. The findings are discussed in the context of the existing literature on habituation, adaptation and contrast-gain control.
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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) | 0.001 | 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".