On the Interference of Task-Irrelevant Hue Variation on Texture Segmentation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although natural images often include discordant information about object boundaries, the majority of research on texture segmentation has involved variation along a single dimension, e.g. colour, orientation, size. In this study, we examined orientation-based texture segmentation in the presence and absence of task-irrelevant colour variation. Previously, it had been shown that orientation-based texture segmentation was impaired if the elements, normally of one colour, were randomly allocated one of two colours (Morgan et al, 1992 Proceedings of the Royal Society of London, Series B 248 291-295). We found that this interference disappeared, however, when the spatial pattern of the colour variation was regular, as opposed to random, and when the elements were randomly positioned. We consider four models of how relevant and irrelevant texture information might combine to produce the interference effect, with special regard to these new findings. None of the models could account for the dependency of the interference effect on the spatial arrangement of colour and orientation in the texture. We suggest that inter-element separation and spatial-frequency selectivity are critical variables in the interference effect.
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.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it