Grapheme Frequency and Color Luminance in Grapheme-Color Synaesthesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it