Convexity as a Cue to Figure-Ground Segmentation in Children
Bibliographic record
Abstract
Adults are more likely to perceive as figures regions that are bound by convex rather than concave contours, a likelihood that increases with the number of regions displayed, revealing an effect of context (Peterson & Salvagio, 2008). To test the development of this convexity bias and the effect of context, we examined the strength of convexity as a figural cue in 5-year-olds and adults (n=24/age). Participants were shown computer displays composed of alternating convex and concave regions that differed in luminance (black or white). Displays consisted of two or eight regions. The task on each trial was to identify the colour of the figure. Both 5-year-olds and adults identified the convex regions more often as a figure for displays containing eight alternating regions than for displays containing only two regions (p <.01), suggesting an effect of context. No difference was observed between age groups (p > .10). To further explore the development of these effects throughout early childhood and to verify that our previous results were reliable, we conducted a follow-up study examining the strength of convexity in 3-year-olds, 5-year-olds, and adults (n=12/age). Instead of a computer monitor, stimuli were presented on laminated cards to make the task more appropriate for the 3-year-olds. Displays comprised four or eight regions. All age groups identified the convex regions as a figure more often for displays containing eight regions (90%) compared to displays containing only four regions (81%, p <.01). There was no significant difference in the proportion of trials on which the convex regions were identified as the figure across the three ages (p > .10). Under the current testing conditions, the strength of convexity as a figural cue when performing figure-ground judgments and the effect of context on these judgments appear to be adult-like by 3 years of age. 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".