When “3” is a Jerk and “E” is a King: Personifying Inanimate Objects in Synesthesia
Bibliographic record
Abstract
We report a case study of an individual (TE) for whom inanimate objects, such as letters, numbers, simple shapes, and even furniture, are experienced as having rich and detailed personalities. TE reports that her object-personality pairings are stable over time, occur independent of her intentions, and have been there for as long as she can remember. In these respects, her experiences are indicative of synesthesia. Here we show that TE's object-personality pairings are very consistent across test-retest, even for novel objects. A qualitative analysis of TE's personality descriptions revealed that her personifications are extremely detailed and multi-dimensional, and that her personifications of familiar and novel objects differ in specific ways. We also found that TE's eye movements can be biased by the emotional associations she has with letters and numbers. These findings demonstrate that synesthesia can involve complex semantic personifications, which can influence visual attention. Finally, we propose a neural model of normal personification and the unusual personifications that accompany object-personality synesthesia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".