Similar perceptual costs for dividing attention between retina- and space-centered targets in humans
Bibliographic record
Abstract
Visual-spatial attention enhances the perception of behaviorally relevant stimuli. One issue that remains unclear is whether attention is preferentially allocated to stimuli that remain fixed in one reference frame (e.g., retina-centered), or whether it could be equally allocated to stimuli fixed in other frames. We investigated this issue by asking observers to covertly attend to sinusoidal gratings fixed in different reference frames and to discriminate changes in their orientation. First, we quantified orientation discrimination thresholds (ODTs) while subjects pursued a moving dot and either attended to a retina- or a space-centered grating. We then measured ODTs while subjects divided attention between the two gratings. We found that dividing attention proportionally increased ODTs for both target gratings relative to the focused attention condition. Second, we used the same stimulus configuration and conditions during a fixation task. Here, one grating was retina- and space-centered while the other moved in space and on the retina. Again, ODTs during divided attention proportionally increased for both gratings. These increases were similar to those measured during smooth pursuit. Our results show that humans can proportionally divide attention between targets centered in different reference frames during both smooth pursuit eye movements and fixations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".