Grapheme Frequency and Color Luminance in Grapheme-Color Synaesthesia
Bibliographic record
Abstract
Individuals with grapheme-color synaesthesia experience vivid colors whenever they see, hear, or just think of ordinary letters and digits (Dixon, Smilek, Cudahy, & Merikle, 2000; Mattingley, Rich, Yelland, & Bradshaw, 2001). Currently, little is known about how specific colors become associated with specific letters and digits in synaesthesia. Beeli, Esslen, and Jancke (2007, this issue) report an interesting relation between grapheme frequency and the luminance and saturation of synaesthetic color experiences. They had 19 synaesthetes choose colors for spoken digits and letters from a digital color palette. The colors were quantified in terms of their hue, saturation, and luminance (the HSL color system). The results showed (a) that the luminance of synaesthetic colors increased with the frequency of digits in everyday language and (b) that the saturation of synaesthetic colors increased with increased letter and digit frequency. These findings indicate that there is a relation between how graphemes are encountered (and perhaps learned) in language and the basic qualities of synaesthetic color experiences. To assess the replicability of the findings reported by Beeli et al., we analyzed the grapheme-color pairings we have collected on-line over the past 5 years for large groups of synaesthetes and nonsynaesthetes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".