[no title]
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 richly detailed, personalities. TE reports that her object-personality pairings have been there for as long as she can remember, are stable over time, occur independent of her intentions, and that this is true even for novel objects. In these respects, her experiences denote synaesthesia. We show that TE's object-personality pairings are indeed consistent over time; she correctly recognized 91% of the personality attributes for familiar objects (3.4 SD greater than the control mean of 47%), and 80% of the attributes for novel objects (2.3 SD greater than the control mean of 57%), when presented with a selection of attributes previously provided for the same or other objects. A qualitative analysis of TE's personality descriptions revealed her personifications are extremely detailed and multidimensional, with familiar and novel objects differing in specific ways - familiar objects having more social characteristics than novel objects in particular. We also show that TE's visual attention can be biased by the emotional associations she has with personalities elicited by letters and numbers. In a free viewing task the valence of TE's object-personality associations had predicted effects on object fixation tendencies. On average, TE fixated negative objects less often than positive objects. She also demonstrated attentional capture by negative objects, fixating negative objects longer than positive objects. Controls showed no significant differences. These findings demonstrate that synaesthesia can involve complex personifications for inanimate objects, which can influence the degree of visual attention paid to those objects.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".