Bibliographic record
Abstract
Biases in the judgement of lengths in the plane abound; examples include the Muller-Lyer illusion and the vertical-horizontal illusion. Here we demonstrate a new form of bias induced by shape. There are two reasons to predict such a bias. First, some theories hold that a shape is neurally represented as a deformation process. Such a mechanism might induce artifactual distortions in the perceived metric structure of the image. Second, if the shape induces a 3D percept, this might bias 2D judgements. Methods. Observers are presented with a triplet of points, in proximity to the outline of an animal shape. Each triplet forms an isosceles right-angle triangle with a horizontal long side. Although the two oblique sides are equal, observers are asked to judge which appears shorter. Interpreting each judgement as a distortion of the perceived triangle, from a sequence of trials we derive a vector field of perceived distortion over the image. In a control condition, observers make the same judgements in the absence of the outline shape: the difference in the resulting vector fields provides an estimate of distortion independent of static inhomogeneities of the visual field. Results. For three of four observers, distortion was found to be significantly larger inside the shape (figure) than outside the shape (ground), and in these same three observers, the distortion grew larger nearer the contour. For two of the observers distortion was significantly biased to flow radially out from the centre of the shape in both figure and ground regions; for the other two observers this radial bias was significant only for the ground region. Conclusion. These results show that shape outlines can induce distortions in the image. These distortions may be artifacts induced by cortical mechanisms for shape representation, or illusions based on 3D perceptual interpretations of the image. Meeting abstract presented at VSS 2013
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 | 0.002 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".