Do you see what I hear? Vantage point preference and visual dominance in a time-space synaesthete
Bibliographic record
Abstract
Time-space synaesthetes "see" time units organized in a spatial form. While the structure might be invariant for most synaesthetes, the perspective by which some view their calendar is somewhat flexible. One well-studied synaesthete L adopts different viewpoints for months seen vs. heard. Interestingly, L claims to prefer her auditory perspective, even though the month names are represented visually upside down. To verify this, we used a spatial-cueing task that included audiovisual month cues. These cues were either congruent with L's preferred "auditory" viewpoint (auditory-only and auditory + month inverted) or incongruent (upright visual-only and auditory + month upright). Our prediction was that L would show enhanced cueing effects (larger response time difference between valid and invalid targets) following the audiovisual congruent cues since both elicit the "preferred" auditory perspective. Also, when faced with conflicting cues, we predicted L would choose the preferred auditory perspective over the visual perspective. As we expected, L did show enhanced cueing effects following the audiovisual congruent cues that corresponded with her preferred auditory perspective, but that the visual perspective dominated when L was faced with both viewpoints simultaneously. The results are discussed with relation to the reification hypothesis of sequence space synaesthesia (Eagleman, 2009).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".