Bibliographic record
Abstract
In static displays, search is inefficient when participants are looking for targets that differ from distractors in line-orientation for objects defined by Kaniza-style illusory contours (disconnected pac-men lined up to create the impression of contours); the same search is efficient for objects defined by actual (connected luminance-based) contours (e.g., Li, Cave & Wolfe, 2008). This suggests that illusory-contour defined line-orientation cues cannot guide the attentional focus to targets in the same way as the same cues can when targets are defined by actual contours, and this in turn may indicate more attentional demands when defining objects as wholes. However, it is unclear whether illusory-contour defined targets are also more difficult to track than those defined by actual contours when items move. On the one hand, the Gestalt cue "grouping by common fate" may be such a powerful indication of object-hood that it wipes out any benefit of real over illusory contours in multiple-object tracking (MOT). On the other, it is possible that static and dynamic cues both contribute to helping objects maintain their integrity during MOT; thus there may be attentional costs to having to draw together the disconnected elements in illusory-contour figures: one that may only reveal itself once the tracking task becomes demanding (with more targets). MOT performance was compared for 1, 3, and 5 targets when all items (targets and distractors) were defined by actual as compared to Kaniza-style illusory contours. In a second experiment, the task was made even more difficult by making the pac-men inducers within individual Kaniza figures differ in contrast polarity (some darker than the background and others lighter), a difference that promoted grouping parts of targets with parts of distractors in static displays (i.e., grouping by similarity in terms of contrast). Results underline similarities and differences between static and dynamic tasks. Meeting abstract presented at VSS 2014
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".