Contributions of gaze-centered and object-centered coding in a double-step saccade task
Bibliographic record
Abstract
The position of a saccade target can be encoded in gaze-centered coordinates, that is, relative to the current gaze position, or in object-centered coordinates, that is, relative to an object in the environment. We tested the role of gaze-centered and object-centered coding in a double-step saccade task involving the Brentano version of the Müller-Lyer illusion. The two visual targets were presented either sequentially, requiring gaze-centered coding of the second saccade target, or simultaneously, thereby providing additional object-centered information about the location of the second target relative to the first. We found that the endpoint of the second saccade was affected by the illusion, irrespective of whether the targets were presented sequentially or simultaneously, suggesting that participants used a gaze-centered updating strategy. We found that variability in saccade endpoints was reduced when object-centered information was consistently available but not when its presence varied from trial to trial. Our results suggest that gaze-centered coding is dominant in the planning of sequential saccades, whereas object-centered information plays a relatively small role.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".