Vertical and diagonal Kanizsa illusory contour targets in an enumeration task
Bibliographic record
Abstract
Every day we view scenes wherein the contours of objects are not wholly visible either because an object is behind something or in front of a background homogeneous with itself. Illusory contour figures (items that can be seen as whole though they lack some of their bounding contours) can be used to study how the visual system deals with objects that are not fully visible. Research indicates than an orientation-based visual search task where rectangles were defined by Kanizsa-style illusory contours (induced by “pacmen” alone) was inefficient. In contrast, when the same rectangles were defined by real contours as well, search was efficient. The goal of this study was to determine whether these results replicate to a selective enumeration task, where participants had to enumerate targets among distractors. In enumeration, it has long been known that people can use a fast, accurate process called subitizing to enumerate small numbers of items in many situations. Generally, subitizing is evident in selective enumeration when targets “pop out” of distractors in the corresponding visual search. Because real contour Kanizsa figures promoted efficient search whereas illusory contour Kanizsa figures did not, we hypothesized that subitizing would only be evident in selective enumeration when participants were enumerating real contour figures. Participants enumerated 1-9 vertical targets in 4 or 8 horizontal distractors when items were defined by real as compared to illusory contours. As expected, the Kanizsa illusory contour figures were not subitized. However, surprisingly, the corresponding real contour figures were not subitized either. This result replicated when the targets were diagonal rectangles. This discrepancy between visual search and selective enumeration may indicate fundamental differences between tasks as it relates to processing complex figures. Meeting abstract presented at VSS 2015
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".