The automaticity of vantage point shifts within a synaesthetes’ spatial calendar
Bibliographic record
Abstract
Time-space synaesthetes report that time units (e.g., months, days, hours) occupy idiosyncratic spatial locations. For the synaesthete (L), the months of the year are projected out in external space in the shape of a 'scoreboard 7', where January to July extend across the top from left to right and August to December make up the vertical segment from top to bottom. Interestingly, L can change the mental vantage point (MVP) from where she views her month-space depending on whether she sees or hears the month name. We used a spatial cueing task to demonstrate that L's attention could be directed to locations within her time-space and change vantage points automatically - from trial to trial. We also sought to eliminate any influence of strategy on L's performance by shortening the interval between the cue and target onset to only 150 ms, and have the targets fall in synaesthetically cued locations on only 15% of trials. If L's performance was attributable to intentionally using the cue to predict target location, these manipulations should eliminate any cueing effects. In two separate experiments, we found that L still showed an attentional bias consistent with her synaesthesia. Thus, we attribute L's rapid and resilient cueing effects to the automaticity of her spatial forms.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".