Spatial modulation of repetition blindness and repetition deafness
Bibliographic record
Abstract
When two identical visual items are presented in rapid succession, people often fail to report the second instance when trying to recall both (e.g., Kanwisher, 1987). We investigated whether this temporal processing deficit is modulated by the spatial separation between the repeated stimuli within both audition and vision. In Experiment 1, lists of one to three digits were rapidly presented from loudspeaker cones arranged in a semicircle around the participant. Recall accuracy was lower when repeated digits were presented from different positions rather than from the same position, as compared to unrepeated control pairs, demonstrating that auditory repetition deafness (RD) is modulated by the spatial displacement between repeated items. A similar spatial modulation of visual repetition blindness (RB) was reported when pairs of masked letters were presented visually from either the same or different positions arranged on a semicircle around fixation (Experiment 2). These results cannot easily be accounted for by the token individuation hypothesis of RB (Kanwisher, 1987; Park & Kanwisher, 1994) and instead support a recognition failure account (Hochhaus & Johnston, 1996; Luo & Caramazza, 1995, 1996).
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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".