Bibliographic record
Abstract
Every day we encounter objects that are partially obscured by shadows or other objects. We can study how our visual system processes these items by creating objects which do not have a real contour (i.e., a solid line) completely surrounding them. This project used enumeration (determining the number of objects present) to study the way that illusory contour figures are processed. The processing of illusory contour objects has previously been studied using a visual search task (Li, Cave & Wolfe, 2008). Li, et al. (2008) found that line-end illusory contour figures (figures that are induced through the particular way lines end around the "object") pop out in visual search. Because objects that pop out in search are usually subitized (fast, accurate and effortless enumeration of a small quantity of objects), it was hypothesized that line-end illusory contour figures would be subitized both when they were presented only as targets (simple enumeration task) and when they were presented with distractors (selective enumeration task). The simple enumeration task required participants to enumerate 1-9 vertical line-end illusory contour rectangles or real contour rectangles presented in conjunction with the line-end inducers. The selective enumeration task was the same expect for the addition of 4 or 8 horizontal distractors of the same figure type. The results of these two enumeration tasks revealed that line-end illusory contour figures can be subitized when they are presented alone, but not when they are presented with distractors. These results may be due to differences in task demands which can be explained by Pylyshyn’s (1988) FINST theory. Meeting abstract presented at VSS 2013
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".