Improved classification images with sparse priorsin a smooth basis
Why this work is in the frame
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Bibliographic record
Abstract
Classification images provide compelling insight into the strategies used by observers in psychophysical tasks. However, because of the high-dimensional nature of classification images and the limited quantity of trials that can practically be performed, classification images are often too noisy to be useful unless denoising strategies are adopted. Here we propose a method of estimating classification images by the use of sparse priors in smooth bases and generalized linear models (GLMs). Sparse priors in a smooth basis are used to impose assumptions about the simplicity of observers' internal templates, and they naturally generalize commonly used methods such as smoothing and thresholding. The use of GLMs in this context provides a number of advantages over classic estimation techniques, including the possibility of using stimuli with non-Gaussian statistics, such as natural textures. Using simulations, we show that our method recovers classification images that are typically less noisy and more accurate for a smaller number of trials than previously published techniques. Finally, we have verified the efficiency and accuracy of our approach with psychophysical data from a human observer.
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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 it