Analysis of Color-Specific Visual Processing Speed Differences in Division 1 College Football Players
Bibliographic record
Abstract
IntroductionThe University of Cincinnati has been doing NeuroVisual Training (NVT) as part of an injury prevention and performance enhancement program since 2010. We recently noticed that some athletes have substantial differences in visual reaction time based on color, specifically red versus green. We set out to assess if they may have had any color processing deficiencies.MethodsWe identified 4 out of 107 screened athletes with deficiencies in their ability to react to green compared to red. After identifying these color deficiencies, we developed a protocol to assess and manage the said deficiencies. The protocol included assessing for color blindness with the Ishihara plates, color Visual Evoked Potentials (cVEP), and color-based visual reaction times.ResultsNone of the individuals had color blindness based on the Ishihara plates. There were significant differences in visual reaction times for red and green with red being significantly slower. cVEP mean red P100 latency was 115.5 ± 3.2 ms versus 104.4 ± 1.3 ms for green, and mean voltage was 7.30 ± 1.4 µV versus 9.20 ± 1.4 µV for green.
 DiscussionNVT is becoming a mainstream means to improve performance and safety for athletes in competitive sports. It was interesting to note that high caliber athletes in a division 1 college football program were showing relatively slow visual reaction times. We were able to train them to a higher level of NVT proficiency once we included color-based tasks that best suited their ability to see and process quickly. People performing NVT on athletes may wish to be aware of and consider checking for color processing deficiencies such that one can train the athletes to the highest level possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".