Forest, Trees and Leaves: Interference Effects in 3-Level Navon Figures
Bibliographic record
Abstract
Navon's seminal findings on global-local attention suggested that global forms dominate perception. One reason why this might be the case is that, in Navon's figures the local elements are surrounded by other similar elements while the global level is alone in space. To test this possibility, we examined interference effects in 3-level hierarchical figures, which consisted of a large configuration made up of medium configurations, which were in turn made up of small figures. In this stimulus, the small elements are local to the medium elements, which are in turn local to the large. Subjects were asked to identify digit or arrow targets at all three hierarchical levels under conditions where the two response-irrelevant levels could be compatible, incompatible or neutral with regards to the correct response. Based on previous research, one would expect the medium elements to dominate the small, due to the greater globality of the former. Contrary to this, we found mutual and equal interference between these two levels under these circumstances. We suggest that this is due to the presence of the large level, which serves to equalize the degree of flanking at the global (medium) and local (small) levels of our stimuli. Previous work has found that flanking elements can have positive or negative effects on response latency. By equating the amount of flanking at the two levels in our 3-level stimuli, we have eliminated global dominance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".