Focusing and orienting spatial attention differently modulate crowding in central and peripheral vision
Bibliographic record
Abstract
The allocation of attentional resources to a particular location or object in space involves two distinct processes: an orienting process and a focusing process. Indeed, it has been demonstrated that performance of different visual tasks can be improved when a cue, such as a dot, anticipates the position of the target (orienting), or when its dimensions (as in the case of a small square) inform about the size of the attentional window (focusing). Here, we examine the role of these two components of visuo-spatial attention (orienting and focusing) in modulating crowding in peripheral (Experiment 1 and Experiment 3a) and foveal (Experiment 2 and Experiment 3b) vision. The task required to discriminate the orientation of a target letter "T," close to acuity threshold, presented with left and right "H" flankers, as a function of target-flanker distance. Three cue types have been used: a red dot, a small square, and a big square. In peripheral vision (Experiment 1 and Experiment 3a), we found a significant improvement with the red dot and no advantage when a small square was used as a cue. In central vision (Experiment 2 and Experiment 3b), only the small square significantly improved participants' performance, reducing the critical distance needed to recover target identification. Taken together, the results indicate a behavioral dissociation of orienting and focusing attention in their capability of modulating crowding. In particular, we confirmed that orientation of attention can modulate crowding in visual periphery, while we found that focal attention can modulate foveal crowding.
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 | 0.002 |
| 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) | 0.001 | 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".