The SHINE toolbox for controlling low-level image properties
Bibliographic record
Abstract
Visual perception can be influenced by top-down processes related to the observer's goals and expectations, as well as by bottom-up processes related to low-level stimulus attributes, such as luminance, contrast, and spatial frequency. When using different physical stimuli across psychological conditions, one faces the problem of disentangling the contribution of low- and high-level factors. Here we make available the SHINE (Spectrum, Histogram, and Intensity Normalization and Equalization) toolbox written with Matlab, which we have found useful for controlling a number of image properties separately or simultaneously. SHINE features functions for scaling the rotational average of the Fourier amplitude spectra (i.e., the energy at each spatial frequency averaged across orientations), as well as for the precise matching of the spectra. It also includes functions for normalizing and scaling mean luminance and contrast, as well as a program for exact histogram specification. SHINE offers ways to apply the luminance adjustments to the whole image or to selective regions only (e.g., separately to the foreground and the background). The toolbox has been successfully employed for parametrically modifying a number of image properties or for equating them across the stimulus set in order to minimize potential low-level confounds in studies on higher-level processes (e.g., Fiset, Blais, Gosselin, Bub, & Tanaka, 2008; Williams, Willenbockel, & Gauthier, 2009). The toolbox can be downloaded here: www.mapageweb.umontreal.ca/gosselif/shine.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.147 | 0.066 |
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".