Shape recognition: convexities, concavities and things in between
Bibliographic record
Abstract
Previous studies on shape recognition have drawn different conclusions regarding the importance of specific object features, such as convexities, concavities and intermediate points. Some studies found evidence for a predominant role of convexities, whereas others favored concavities or intermediate parts. However, most studies have employed familiar objects or simple geometric shapes not necessarily containing curves (polygons) as their stimuli. Here we present a novel set of shapes with well-defined convexities, concavities and points between convexities and concavities. The shapes were composed of the sum of three different radial frequency (RF) components with random phases, segmented to remove all but variable lengths of contour centred on the feature of interest. Observers were required to match the segmented test shape to one of two subsequently presented whole-contour re-scaled test shapes. Observers were never presented with the same shape twice. Results show that for short (dot-sized) segment lengths, performance was significantly higher for convexities than for either concavities or intermediate points. For the convexities, performance remained constant as a function of segment length, and although performance improved with segment length for concavities and intermediate points, it only reached convexity performance at the largest lengths tested. No significant differences between concavities and intermediates were found. We present a model by which positions of convexities are extracted and connected to form shape primitives (polygons) that are matched to the test shapes. These results indicate that these closed shapes are encoded from the positions of convexities, rather than from the positions of either concavities or intermediates. Meeting abstract presented at VSS 2015
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".